{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一.算法介绍\n",
    "学习向量量化（Learning Vector Quantization,LVQ）是在标签的基础上继续做聚类，打个比方，比如你的数据已经有“好客户”和“坏客户”这两种标签了，但是你想在此基础上继续将“好客户”聚类为“高、中、低”端这三类客户，而对于“坏客户”你想继续聚类为“一般”和“极差”的客户，这便是LVQ要做的事，算法如下   \n",
    "\n",
    ">输入：样本集$D=\\{(x_1,y_1),...,(x_m,y_m)\\}$，$q$个原型向量的类别标记$\\{t_1,t_2,...,t_q\\}$，学习率$\\eta\\in(0,1)$    \n",
    "\n",
    ">（1）初始化一组原型向量$\\{p_1,p_2,...,p_q\\}$   \n",
    "\n",
    ">（2）重复以下过程，直到停止条件   \n",
    "\n",
    ">>（2.1）从样本集$D$中随机选取样本$(x_j,y_j)$；   \n",
    "\n",
    ">>（2.2）计算样本$x_j$与$p_i(1\\leq i\\leq q)$的距离，$d_{ji}$；   \n",
    "\n",
    ">>（2.3）找出与$x_j$距离最近的原型向量$p_{i^*}$；   \n",
    "\n",
    ">>（2.4）如果 $y_j=t_{i^*}$ 则$p'=p_{i^*}+\\eta\\cdot(x_j-p_{i^*})$，否则$p'=p_{i^*}-\\eta\\cdot(x_j-p_{i^*})$，并令$p_{i^*}=p'$  \n",
    "\n",
    ">>（2.5）判断是否满足停止条件，终止循环   \n",
    "\n",
    ">输出：原型向量$\\{p_1,...,p_q\\}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二.代码实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "原型聚类LVQ的实现，代码封装在ml_models.cluster\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import copy\n",
    "\n",
    "\n",
    "class LVQ(object):\n",
    "    def __init__(self, class_label=None, epochs=100, eta=1e-3, tol=1e-3, dist_method=None):\n",
    "        \"\"\"\n",
    "        :param class_label: 原型向量类别标记\n",
    "        :param epochs: 最大迭代次数\n",
    "        :param eta:学习率\n",
    "        :param tol: 终止条件\n",
    "        :param dist_method:距离函数，默认欧氏距离\n",
    "        \"\"\"\n",
    "        self.class_label = class_label\n",
    "        self.epochs = epochs\n",
    "        self.eta = eta\n",
    "        self.tol = tol\n",
    "        self.dist_method = dist_method\n",
    "        if self.dist_method is None:\n",
    "            self.dist_method = lambda x, y: np.sqrt(np.sum(np.power(x - y, 2)))\n",
    "        self.cluster_centers_ = {}  # 记录簇中心坐标\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        m = X.shape[0]\n",
    "        # 随机初始化一组原型向量\n",
    "        for idx, random_idx in enumerate(np.random.choice(list(range(m)), len(self.class_label), replace=False)):\n",
    "            self.cluster_centers_[idx] = X[random_idx]\n",
    "        # 更新\n",
    "        for _ in range(self.epochs):\n",
    "            eps = 0\n",
    "            cluster_centers_old = copy.deepcopy(self.cluster_centers_)\n",
    "            idxs = list(range(m))\n",
    "            np.random.shuffle(idxs)\n",
    "            # 随机选择样本点\n",
    "            for idx in idxs:\n",
    "                vec = X[idx]\n",
    "                yi = y[idx]\n",
    "                bst_distance = np.infty\n",
    "                bst_cid = None\n",
    "                for cid in range(len(self.class_label)):\n",
    "                    center_vec = self.cluster_centers_[cid]\n",
    "                    if self.dist_method(vec, center_vec) < bst_distance:\n",
    "                        bst_distance = self.dist_method(vec, center_vec)\n",
    "                        bst_cid = cid\n",
    "                # 更新\n",
    "                if yi == self.class_label[bst_cid]:\n",
    "                    self.cluster_centers_[bst_cid] = (1-self.eta)*self.cluster_centers_[bst_cid] + self.eta * vec\n",
    "                else:\n",
    "                    self.cluster_centers_[bst_cid] = self.cluster_centers_[bst_cid] - self.eta * (vec - self.cluster_centers_[bst_cid])\n",
    "            # 判断终止条件\n",
    "            for key in self.cluster_centers_:\n",
    "                eps += self.dist_method(cluster_centers_old[key], self.cluster_centers_[key])\n",
    "            eps /= len(self.cluster_centers_)\n",
    "            if eps < self.tol:\n",
    "                break\n",
    "\n",
    "    def predict(self, X):\n",
    "        m = X.shape[0]\n",
    "        rst = []\n",
    "        for i in range(m):\n",
    "            vec = X[i]\n",
    "            best_k = None\n",
    "            min_dist = np.infty\n",
    "            for idx in range(len(self.cluster_centers_)):\n",
    "                dist = self.dist_method(self.cluster_centers_[idx], vec)\n",
    "                if dist < min_dist:\n",
    "                    min_dist = dist\n",
    "                    best_k = idx\n",
    "            rst.append(best_k)\n",
    "        return np.asarray(rst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三.测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "import numpy as np\n",
    "\n",
    "X, y = make_blobs(n_samples=400, centers=4, cluster_std=0.85, random_state=0)\n",
    "X = X[:, ::-1]\n",
    "\n",
    "# 将0,2类归为0类\n",
    "y = np.where(y == 2, 0, y)\n",
    "# 将1,3类归为1类\n",
    "y = np.where(y == 3, 1, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lvq = LVQ(class_label=[0, 0, 1, 1])\n",
    "lvq.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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bt8Sm9Oe+Bw7SPFzJyWQrFVXwyQdt6dXTG4CMTBMzH5mHq7cPaz9+k9GDnNzzkBtZ2dm89eGraJQW2rZyo7DQRnmFDVEEjVqGp4uN0sLCiwo7QEpcPMGepcx7u1NNKubggRZumfkNHfv1q7f8/padO/GTMB+TrbLmms1po0iWS6d20+plzvrEaHDhqQdf4nRaMkUlBQT5h9ZyJ/2+dzt+hNSIOkCANpRDxWmkZZ2hWVD9VT7/V1jotowu/HdEHaSsmDpHFEU+uHcmaftWc88tMh6YrqI8ZRcf3XMXVeXlF31+8+JFRIbms+nXnsyfF8Wq7zvQM0ZNSmr1+aW5eRb27S+hmZ+Fn95/n3DfYuxWK4lJ5XSNcePpR/yoqrCxblMpVquNAD85wQEKSstspJypxM3LC6hOtTy+ey+7164n89T5/XBOH9nPiMG6GlEHcHdTEuDtYN23i+ut+6Orpwcj751GrHk3WeWJJJfHc9i0k159+9IsOKJe5qxvBEEgIrQFMR17nBcjMJtMKAXleffLBQVWq+VqmtkkiY+Y26iOtKsrpB37ZZKRfIrkw7GotBo69OyO0cMdgOO79mIuSGbjjyH4elcv780TXLl+Sjo7lq9i2G23/O2YoihybMuvfPGOJ1VFOdgsFgSZjDunuvDu/Bw8PVQs+OI0A3prCfV3sG9/LAUKNWF9RY4dL+X7ZRm8+Vp7bE4Z3y4rIzhQwZC+OtKz7Lz7WTEBAQaSj8YSEB7O0lefoXmgmZAgGb+sseDRvBvjHni4JuNFpTVQUnoutTEj08TTz8aB00YYW/jhpU0EdxrA9bPuqXN3Qa/RwzH5eLFv4WYivb3p2G4aYcENU/la33Tq2IWVCSvwE4MRhOqK47zyLHJMmWzfuRmn00nr5u0a2sxGyX/N/fJnJGG/RERRZO3n88k4vIGh/TSUZMH8h79kxL1P0iamC0d/+53rh+hrRB3Ax0vBqKF6Nh7c/8/C7nRSUVaGzObEy0uBTqfGbHFQXgbp6SYWfHGaL97zIzhQyemUKsaP1PHkKwV0i9YxeZyKhctKmPfZaRRqPT2iRfYfsTB/YSlGvYwJ4wJwoGHv6WQO/voTd98Eo0ZUp1TeZ3XyyP/2sX/DZroNrw7qRfYbwPI5axjU30RQoJY33kpi/HA13WM88QlthsXi5KGnd3BoSzuiBw+o83VOzDJxc8w4TFWnWLhkAcUlhYSFtWDMiImEBoXV+Xz1hclcxd5Dv3Mm9TS+vn70jO6Lq4t7zc+7RvVi74FdHD21Cw/Rl4KyPLLsqYQaWlAQW8Inse9z3bCRjBh8QwO+ReNjXtxx+kf8N0UdJFfMJZN06ChFiZtY+Fk4d88I5omHgnn/FV/WfvwmVosFNy8vTGYnfy3oNZmdGDx9/nFsmVwOgoxd+yoxGOTI5aDXyTl+wkZuvpUWYQKmSguJJyowm520ba1jaD8tm7aVU1RsJ7Kdhs1b8wlp15GMfD0nz4iEBGnQ6VWs3VDC3kNVyNVaTAWpjBx2zhaVSsbUSR6c2LWx5lpAeDO6T5rF9AeyuOuBUxyPK6NrtAF3/wAEQUCjkTP1RjcSd9Zf4VR+wVGWLv4O92I/IuU9MSXZeOfDV8nITqu3OeuSkrJiXnrzaTauWE/uwUJ2r93Ni68/RXrWubbMSoWS2Xc+zqRbpyJr5qRYkUdfv+F08IgmRB9BR00P1q5f1aiLtK42c6w76D96DUMj2je0KQ2GJOyXSOKeHYy/Xo9Oey7PuHUrAy3DBU4fi6PX6BFs+t3B4dgqrFYRu10kLsHE6g1mht1689+OW1ZUxJJXX8ReVcyHC4oYcEMKXy4u4v35RXy1tApBEHBzkRMRriEsVINOJ1BYaEWQyfhhdRkTbk/jpTdyMFtEmnXoSEZaMY/f48rCeYEsWxDAzeM0bN+eS4tOnVAoBP7q1VAqZDjt9lrXoocM5r553xDQawZGT098Q0NRqc/lX2vUMuz15Ad22u2s+fUn2mo646n2RS3XEKwPx9cRwq8bV9fLnHXNmvUrUJfoaavvQpC+uvrU1xrKkh+/qXWfQqEgOrIb/j4BtNB3wEV1LritlmtwFTxIPpN0tc1vlMRHzGVMm13cETmZEP1/V9glV8wlIgiyC3ZeFEUQEHD38Wbo3Y8z/ZF36d2lDASR3/aJ9Js6m4DwZhcc0+l0svTVFxjZs5g5d4Xh7W5nx64qXnizkDE3BNKsGQzqo2b7LgvZuXb8fQQCfOUcPmZmyYoyukfreO0ZXyoqHaRkqpj1xCLGjvImLNxI8unq7JKOka707KWmvLgEUe3Ftu0FuLgoUShltG1t4IdVRUR0nXKebRq9ju7Dh3L41+UcOFRK12j3P2wW+WlNCeHRw+psbc9yIC0DfZaFQhvodbWLtTxVPqSmNQ6ROxJ7kFaaqFrXArQh7Epdj9liPq8RmMFgxCqe/23EigWtVlevtjYF/ss+9b/SpIRdFEWO/b6H+O3rsFpMhHXuTbdhQy+pDP9itO7emx+/3cbQwXb0uurli4sv5+QZGB5ZHeTqMXIEbbp2JWHfAURR5L47o3H38f7bMVMTTqAVs7ltajiWKhNFBdkMHWDkVKqDcovAkdhypt0URlCog9vuT2HEID0KBXy5uJSScif3TXchr8COKKho3d6frlEleLla8fD3x+l0gigik8sJDsygqryc1r2G8ujTbxPZRoEowolTdrwjorjr/guLtEwmY8TMh3jujRcZ2qeC0CA5W3ZaKLSHcss9dS/sACqtHlHmwOwwoZFra66X2Irx9W0c1adqtRpbZe3ce4doRyaTIb9AW4YeMX3Ztn0TvrZAjEpXRFEk25yG3CCjVXibq2V2o2TloZO0/A/71P9KkxL29V9/RXH8r0yb7Iper2DV2sUsevE3bn1xTp2VwreI6sipI8O55a61DOqtoaQcfttn4/r7n65Vru/m7UWPkf9O9EoLCghvpq72Xet1iKIv2XmFaNQiXy4rxSe8IyfPlDN8oIEurf04Gm+luNSBSqOkrb8clVqBoHLBzdsbQYCuMR58vyyN22eIyOXV3rYqk4Ode01cF+3LrqWfsejLjgT7iTgddg4dtzB3XhHnBQb+RFj7tsx46xOObNtOWloBYUPbc333GOSKuv8I7TlwmtEafwL7DmH3lt9pqYlEK9dTaM0jk1PcP+jROp+zPujdcwDbftlMO2UMckGOKIqcrkqgS5duKBXnfx79fQKYOmUG333/JSqTFjs2dG46Zs94rLo9gcQFeSXzEOMmbqCtdDBMDU3m01KUm0fijjUs+yocg776taI7u/LAk2c4tmsPUf371Mk8giAwfPoMsgcM5uSRo6i9tMy8uRt6139uwVpVUUHc7n2YKirQu7igUCoJiAjDOzCAwOYRLP22CrPZgUYjR2swoNEbiDsjMvK+Owhp1YKvX3wMlcxMr84CndppeHNeIRNu8GbbzhLSM+00b6Op8ZuXljoptvrzyP/OMH6UC2azk8XLywjvdh0pcXGE+JrYtDmf0FAdgwZ607+fgl82pZG4/xCRfXr+7Tu4enrQb/zV6Vsd2dqfdi0nolKq2LRtHZZKM15ePsy4YRYtwltfFRuulCF9R5CRkcr+2K24yjyoFMvwbxbI5LF/3/wrplMP2rWM5NiJI7i7uNM8rLVUffoPzLHuYFzfXdJO/S80GWFPSzxBTJSuRtShWoQH99GyMeFonQn7WfzDQvEPC734jcDpY3H89PZLdGoDx4/mImCnZWs3dnyjIqhjH264dzYhnQfw2LM7uH2KJwaDgp9/LeJElgczZvdGpdEw4ak5LPt6Pi+9sYPmoXLGjnBh7EgVDquK+5/K4YlHXWnWDHbuKWXVRoEZc98mJS6eL9fsQKZQ0n7MEPyahTDv/jsZ1NVMyxAZh2JL+WF5Bm+9Homnh4CpsvKi71Lf/PnEJLlMzvVDxzFi8BisNitqlbpectkLiws4mZKITqunTYv2F9xNXw4KhYI7b72f7LwssnLS8fTwJjQw7B/fYefebaz4eSkOixOnYKdn975MGH3zeTaZzFWkZZ7BoDMQ4BfcJHP8L8bKQycZM1ES9QvRZIRd7+pKUvb5vUQysu1oXTwbwKJqHHY7q9+fy2tPu7N6TTaTbtAx63Z30jKtyPU+vPD6bnb/0oJRs+5l37oWvPHFr9gsZsI6D2Pay2Nr4gPBLZtz+5w3eGvG7ZSUJnEq1cHcD4s5lmDFzdOVT75XYzDK8WvRj1tfGY3GoKfL4IHEDD13ZuvS115h5q1GRvTV0SxEzcQb4POFxXz2eQr7YmVMuf7ayCLwKFHBn/pfyWSyejlxSBRFft6wnA2b1uKGJzasOHU27r/rcUICm9XZPP4+Af+qK2VswmF++GExbVSdMepcsTosHPn9CIIgq7XL37JzPT/9vAwdBixOE94Bvsy6/UE83Bruc361iU3MpuXE5Rh06ovf/B+kyXzHi4hsT36FKyt/zqk5mDn2eBk/b7QQNWhgg9mVlphEgI+Nli0NHD5SzIyb3ZDLBTxc5YjWSqbf4kX8jl+RyWR0HzGM6a+/z93vfcbQW29FZzRSlJvHL/Pn89WTD7B4zktYyvL4YUl3evUNYcCgUBZ905WXn4tAb9Bw22vvoXdz5+unH+STWVOZd+8MDm/bDlRn3pw6vJ/xY4MRlHrSMqyUldkZ3FfHz2tzadN/zFU7ju9aISH5OFs2bqSLui9tdJ2J1HXHzxTGJ1++Vx10vsps2LyGEFkLjMrqPjwquZpW2o78vmc7lj/SSk+cimfVyuV0VPQgUtOdaG1/xEwZ87/54Krb21CsPHQSxchv8HV3aXJH2tUVTWbHLpPJmPTUiyx+/w2++T4ZvV5BcYWGEfc+jaefb4PZ5XSKyGVgszqRy0Ct/uMr8x//MRoU2MwXdoHkZ2ax6LlHmThcTrfrXDiZnMw7e4o5eqyUgf29au5TyAWcTge/LV9BUez3LHgngKBALQmJ5Tw39wNUGh1tu0WDALkZOehUFhwOEasdikqVGDz9GDx1an0vxb9iz4HTjJZfnT8we/btxJdgVPJzuz4/bRBZ5SmkpJ8iIrTFVbHjLEXFRYQqascP1HINgkWgylSJWqVmx+9b8CcEraL67FlBEGima8W+jK1k52U1qn71l8OfA6U9vfs3tDnXLE1G2AG8AvyZMfcdCrKysZkt+DULqdPTfuw2Gwc2biZpzxYAWnYfSPSQQSiUf++TDW3dktXZctIzTfj5adnyWyUD+xgoLnGgdTOyZkUhYVEX/kaxc/n3TB0rZ8qkar9Eu7ZGPPQVvP3eSfr08kQmE3A6RZauKCQiZhwH1y7ni/cCCAyoTg9s09rIQzNtvPvNQhL37KAkN5eb78hm3PUuzLzNA6fDwbdLS4gacuM15aONbH11hN1qtSAXzv8nIEeBzW69Kjb8mYjw5mQfysagPBeIL7UWodapcTFW7+LLK8pR/yn9E6rFXS3TUGVq+BhJfTOu7wbJp/4vaDKumLMIgoB3YAABEWF1KuqiKPLjW69TfOhL7p9cyv2TSyk+9CU/vv3GBQuWzqJQKRl572M89mIhWoOep1/N58mXcti0086r7xawab+BPuMnXPDZzMSj9O3tAYDdZsdqttC1RzAFRXaefD6Fbxelc9eDKZwuCqNj376Yy4vQCkUU5WRjqqjEbrfj5Woh6cBewuSbWb0wgGWfByIXRMZNS2f2//LZscdE8y4xdbZOV8KfA6dXg86dYsgTM3CK59wuZbYSrAoz4cHN631+m93GydOJpKQl43Q6GTFkLAXqLFIqEim3lZJVlcYJ2xHGj76p5kSlyA4dybdn1frMVdjKsMnMBPuH1LvNDcnZI+0kLs4V79gFQQgGvgX8ACcwXxTF96903GuNM3EJWPKO8ua8sJpDnrtEuTJt1hHOxCcS1u7vC0hadu7E3e99TuzOXbTzzyW9ooLsVDu+rVtzx6x+qLXaCz5ncPMkI70MnawEm6UKpUIgv8iO3ankaJKSA0erCGwXzQ13383qj97FYa2gIF9Py3AV+YXZ2GwiK1eVEtlGyZ1TNBj1Ah6eWh6freVMZjZRnQNoWwSF2Tk079ihXtbtUvlr4LQ+6RLZjX2H9nAkYRcudk+sThMlqgKV/24IAAAgAElEQVRmTJuFSnXhoFxRSQHHEo8gE2REtomq1dDrUjgaf5Cvv5uPwqbCiQOVUcWs6Q/w5EMv8Oum1Zw6fRIvT2/uHjSbti3O/W56dx3A7r07ics5gJfMH7PTRJ6QzqRJU//W5sZObGI2ipHfoFQpmNb6wpsgidrUhSvGDjwiiuIhQRCMwEFBEDaKohhfB2NfM6QmnKBfd1WNqAMoFDL69VBdVNgBDG6u9Lx++HnXbVYru3/5lZSDvyFXamjXbyjtenRDEAQ6XTeGN959gbefcyUkWEllpZMvFhahVZp5brYSb59A1qyL4/NHZ9PM38pTj7VgznupPDzLDVcDHIs3sXp9JbdNdsXNTYmpyobTbkerVdIjWocFSDhpo2u3c64PURQ5uHkLh9f9RF56NkqDB0Gt2xHZszutY7rU+5mnf4coiiSnnCDpdCJ6vYGodtEUlxZid9gJDQq/rBRFuVzBrTfewRsfv8Sp1DjUcg1yUUZ6ZhpR7WPOc09t27WRH39agrvojSiILBO+4+ZJt9G9y6Wl0hYU5bPgm3m0kXXGVVv9jSynPIMPPnuT1559j9tvmvm3z2rUWh67/zl2H9jB8fhYvFz8mNJjCmEh9f8NoyFYeegkLScux9fdhVHBIxranEbDFQu7KIrZQPYf/18uCEIC1XuuJiXsBnc30o+d73JJy3RiiLz4iUQXwmG3893LLxCkP8VdY12pqnKw6Me3yTgxnGG3Tye4ZQsWpli48c4MwkOU5BXYKSl1sOa7YLRGJx7+Bh5uYSAjLRatVsGIYX4YjUq+WJrB7j2F9IjWMv3WQI4fL8ZoUFBcbKWk1IaHp5LDxyzYqKBCDCOsfVugWjy3LF5C9sHlqC2ltA600ap5CTv3xrFix3J0XiFMefbVv+15c6lkJJ9i++KvSI0/jt7FhWKvNtzW4vwzTR1OB18smkfCsThcHV6YnZV8VvE+bgYPDCojDpWd22+5mw6toy4wyz+z6IcvkeerGOI9Dpkgw+ows23TZgL8A4np2KPmvryCHJb/tJROql5oFdV9Wyrt5Xz3/de0btEeNxd34k8eY/vOzVRWVhDZvhN9ug9Eqzm/x8u+w7vwsPvgavCoueanDSLflElcUiyd2kX/o80atYYBvYYy4E/npjZVzqY0SqJ+adRp8FQQhGZAFLC3Lse9FmjfoxufLFnA1u0F9O9bnS+8bUchB+NkzJrZ/bLGjNuzH1dOMffFZshk1bvD7t3cmTR9De7+waz//EM0CivLPvdDIZdx4rSNvQdNqFRgt54L7vXpbuCH1dVtXfv08qRPL09uv+MAt0zQ07ajP3sPlPPhgiJuHG0kOcXM6s9y2LDdRPTwodw0+25Ep5NNS5ZweP0qCtLT8PZWMHKQlifv90atlpNXYOPDL8o5k1XMT+/OYdb7n15xNWR+ZhY/zHmaB2bo6fdCC3JyLbz89m5Opxrp1Pa2WvcePLqHpNhEonS9kSEjMzsdtcNAekUyPQN6UGYr4fOvPub5J+fi6e514QkvQHllOfEJsXTVDUQmVL+PSq4hRNacrds31hL2w3EHcHf61Ig6gF5hxNXiybGEw1gsFn5evYIAwlDLdWxL2crufTt54oHn0ahru9oqqypQiCr+ikJUUWWq+tf2N3XONvWSUhovnTqLRAiCYACWAw+Kolh2gZ/fJQjCAUEQDpQXl9TVtFcNjV7HxKde4oNFSsbfmsz4acl8uEjFjU+/jEZ3YR953O59LHzuST6ZfSerPv6QgqzsWj9POXqIEH87m7YWkF9Qnads0Cvo2knNmnnvMm2ijh7RGtq2VNO+jYqwIDmpGTaMekA4FxjOypdzKg3i4quXXRRFAoP1fPxVGaJcxeuvtqegTMuwyRlMf6yKM+IInlq6komPPo7BzZV1X3yOmLaaL97xYuOPwchwMKy/BqdTQACMejkTrteTmlqBQVFM1qmUK17PvWtWM3m0mmFDfdBq5fgEKHjmcX9iEzZTXln7CMF9B3bjJ4QgF+SYLSYEp4CXwg+FU0GprQg3lQdudm/2Hd51STZYLCZkKJDLau9v1HINlZUVta6JThFBPD9zSEDAZDGx6pcf6aDpTpA+DG+NP231XTDnWtm1f8d5z7Rp0Y5iWX6toK3NaaVELKBlI2mXUN+cDZRKGTCXR53s2AVBUFIt6t+JorjiQveIojgfmA8Q3r7t36eRXMMUZmXjtFkpzDMhCiq6juqGf1izC967e80vJG74knunexAUpGXH7/tZ+Oxups15Dw9fH9ISkziyeS1i8ypkjgo+/dTMjTcGM3liEGdSK4jppCY4WEdmmor0LDteHnKahyuRCfDlknLGj3dDFEX27i9h8YoyjG5u3HzbYXQ6OVqDEaNvKJ4hPZl4215aRGhJOSPQpv8oxtz/UK1ul5WlZSTu2sTyr8PQagQK0vPRqAW8veSUlDrQaWWYLSIatRyHo7oHu8165amAxZmn6DhIX+uaq1ZFcICKgsJcjPpz7XplcjlOqnvFO50OhD+KAERBrPl/paiiovLiZ8r+GQ83L3RGHXkV2ZTZisiryq4WeTn07t6bLTvXs2vPDuwOOy1btqFAlk2wPQKToxIQUct0FAv5uBnd0GFAKz+3mxcEAS+5P3EJxxjY+7pa87ZtGUnzNs05mrAbXyEIh+ggl3QGDhiKl8c/H8byXyA+Yi4GnVraqV8BdZEVIwBfAAmiKL5z5SZdmyTsP8ieJe8y+Xodv6y1IKOKIys/I277RmbMfQd333P/IG1WK7t+/JYv3gsiKLB6N98sVIfdlsnuVT9x3e3TWfHWy7z5gh8RfmUEBSiprHIy/cEMSkrtnEpXMHGUlm4xbsz//BQ2p4JKk0hlpZO7p/tzz+O5LN+Uj15fRrlJibsrDOjlwGFxRXTa2LqrHI2rL+MffJiUuATSEpMYPbEDoa1bnfdexfn5BPgpMRiqPwoqnYEuHbWsXl/JDcMNVFQ5KSxysGG7mdatjcSnKghuceWBOreAMI7F76FTR9eaaxWVdtKzrOeJW7foniyO/wY/ZyBqlQYHhRQ6cxFlIq5KD5yik2JZPm1anBOC0vISKirK8PL0RSYIKJXnuz5kMhmTx93C6++/hJvNG19ZEFbRQpbsDLsO/IaySkOwMgKZIOPYb8dwyu1syF6OzmlEEAQqhXLGjJpAoF8wFqcJURRrBVzNjiq8Xc8vGJLJZNx922wOxO7l4KF9qFRKro8ZSbtWHa94XRs7kvulbqiLHXsvYCpwTBCEI39ce1oUxbV1MPY1w75VS7llvJ7FS9J47RlPOkdqMZmcfLSggKVzXuDudz+q8TsXZufg5U6NqJ+lRzcXNnx4nNPH4gj1t9GzRyDmSj0Z2bko5CL9uqv5+kcT4x5+ii3fvsntU2XcdWc49z1xmj7dVZjNTrbuU9J78gx63jAWq9nMoheeRijPwlZpomM7NUfiRFz0ImkHN/L61ON4uQuEBqtZsW4xLXtex3W3T69VUOXh50tWjp2SEhtubkrcvH2YMtnBzAeS2LbLREwnDSdO2Uk46UBUeTHqwYfqJDOm28gb+O65bfj65NO/jydJSaV89VkR7Vv1x2io3Skzqn0M8V2PsX/fDtxEL0rUheSaM2mp70C2KZ08MYPw1uG0bRmJyVzFt99/TuyxI1RVVVFpLken1dM8vBWTxk2lRVjtP25mq4UAQwih9tY4HHY0Gg2hinC2ZqxhgP9IDH+U9+vlRtZn/kiUsSduCk9AwCKvYu++XYwaMh6fAD9SMk8QpmuFIAiU20rJlWVwc88LV/TK5Qq6RfWiW1SvK17LpoJ0UEbdURdZMTupKZBvuhTnZJGWbmXUUB2dI6sFW6uVccNwPb8fyyc14URNyqPR3Z3CIjuVVecO4wA4k2rC4BWBzWpDp6v+I6DR61DrmmGzWPEOUNFpQAwdenUnI3E4dz2wjsF9VJRVOPhlQyXBwRq8vLSciT1Er7HjcPfxJvtUEs8+oOfm8UaUCoEp42De1yW8Na8InS2bD+fG4OkO2Wm5PP7CIp5buYLAlm0wGlXoPbzpPPR6IgeN4qmX1vDgTF8C/DUknJIj6IPw6Dae3alnsFBJxzFtiRkyuNY3kyvBJziQ8U++ysLFX/LK23FUOQVGd53AkD6jzrtXJpMx9cY76N97CCdTEtBp9CiVSg4e2Y/VamV850nEdOyOTCZj4fcLSI/NRGc2IpgVtBG64DA5IMPBR5++xRMPP0+Ab1DN2EknE/CW++Ppci7oWlFZjhuelNlK0CmMVNrLyTVn4S764CJzx8V4NgvKlYLKbI7EHeCeGQ/x2dcfsC99KyqZCrvcxuhR48jJy6K0rJi2LSPrrGtkU0QS9bqlSbUUqE98mkVw8uR+xo8494/TbHEiyBUEByr4c0BY72IkvEsf3np/Dw/fF4TRqOBEUgXzF5Zy3X1j8A9rxq+f2MjINBEUqEUQBAS5kl82W2gzujrDZtj0GSQf7cqS915n3ChPbp/W7I9CJpF3Pkxn65IlDJ8xA6fDwXUDdPw5SWXUED3vflpEt2g9CYklZKfnYbc5GTvcwIZtFXi7x2J1ahjYyZevPthDzPhZ6Nym8cTcVVQU5xDctgNTnr+VgIiwel3TkFYtmPria+xPTWfvwRSGBXb+x/uDA0IJDjjXKrlLZO1spPKKMmKPH6GDuju7ijcSJeuNXFBgdVqRWcFbHsiWHeu5ZeKMmmfc3Nw5JdYOBstkMkxUUmYtJbZwP3aHDYtoxhPf8yof5U4lVaZK3FzceWL28+QV5FBZVcHR44dY+fMPuOGFTbDi0Fi5/67HCA2q3zVtjEiiXvdIwv4v6TluCl8/vR+tqoKRQwxYrCK5eXZQeXAoNp/bbqntdx5x9yzWfSFn2IT14LQhVxsZMXM24R2qj88beOs9zHzkI8YO0+LqIuOXTSYUvtG07VZd3i8IAmHt2mC3VDLttpYIMoGt26uzZzq117L+k834hTVHplCRlmFHqxFwMcgwmUXOZNhwc5WTcMLC0WPJ9OmmJqKZkq2/V3DylIUF7wUy6c5senR3I7KDkZmPLWD2ZwvpNfr6q76uZ9+1LipOKyrLUQlqbKIFjaCr6QMjF2TY7TaMCjeysjNrPdMzph+bt6yjwJKLp8oHEZFcZyZWpYXE4iNo0RMohGHHThYpnCqNp7OxJ3a7jQpTBTm2dAL8bqwZz8fLj8TkOLZt2UQXTd+aBmO5pkw++eJdXn323Zr2ABKSqNcXkrD/S5q1bc3NL7zOd88/wazHchg1zA2boGPpyiLa9Lv+vDNNRaeTktwcmoVo6NLBSFKKk72rvqd5xw64eHjQedAAglq24Mi2bdiKq4iaFE3Lzp1w2O389NkXpB7+DXNFJVmp2Xz8kZV9h6po3VxJeKiSRb9Wkn3KTPHBz2gVIWfxT+XMuMmAQiFDpRJYsbYKpVJOQZGNlx73YPR1euQygeuH6Hjx7SJ++qWMkCAl2TkWItu74OMpkpOWRlDziAZaXagozOXtT1ZyIjkBF6MrY6+fRK/ofpc0hpenDyhFcIiYxSpsohWloMIm2tBqNBTacmgTWjud0MvDm1l3PsTXi+eTUh6PQ3QQHBpKB2dH4g4eox0xKFAiyAQ88SXWthtljgqH1UEB2chUMuZ/+QHTb51FVPvqP8p79u/ERzzXNVJ0OnER3EkpTuRkSiKtI9rVzaI1ciRRrz8kYb8EWnSK5MklK9i79lfmr9mJUqOl3YhhdOzbu9Z9hdk5LHntVSg5zsjhPowZ7Yevj5oFX2ey/ovPmPjYUwC4eHjQe+wYdAYDUF208+E9d9C2WSUTB6r4cXU5Iwap+XVDIbfeaOSWiW64u6sZN1zNc28U0amdgmcf68jTz8Ux+5kiWjXXcji2isIyJeHRAyg6vJmuURrKyh2YzCKVlXDHza68+XEJeYUCIUFabDYnRUU29Mbq9MKc1DQyk0/j5u1FWPu2Fy1EKszJ5fDmzZQX5FBZacdRlYtMrqR1r8F0GTTgXzVi2/LLLk5/Nx+VVYEvQdiKbHz48ZucGn6CW2+861//fpQKJePH3MSyZYtwUbuRYD5EgBCGUqbEIq+gRJPPwD4P1HrGZrdRXllGm1btkcvlxET1oFVEGx598V58ZUG12uO64YmHzYdkWxzB2nDaG7rgqwmi3F7KV4s+pdXzbdFp9Vht1prc+MqqCoqKC5CJciqdFXzw2Rs8NOvp84K4/zXmWHcwBknU6wtJ2C8RjU5Lvwnj6DfhXOl75qnTrP38U7JPJiBXqnBaKxgzBPr1cOV4Yjn3P3iE1+d0YMokPxZP3ktBVjYbv5pPRsIRBER8wlsz7M77+OHNOXjoyvh4biBT70ln/tu+eHnKmDIzh9snu5CdZ6W41InVInL/nV689Uk+UyYF8cE7kSz7MYv538Okl54hpFULKktK+Wx2LFUOF0pyStHrZQQEKMg+XMnJ01YmTgzFYJCz4OssPMM64OLpwYr33iYnfhfRnbTsW29lk92Xyf97EVdPjwuuRfLRY/z83suMGqJiV1IObkY7o4e5oXf3ZsmKz1hx/AgTHr74wdOZG9ahsiqIkvdB8Yf7xM8ZzNr1P3PDsBtxdfn3LRt6xfTD08OLzVvXkZSSSI4pBaVSSdtWHZg+8g68PM59s7JYLbz36Wvkpxagt7hS6ahg246NzJzxIAH+AaSkpNVKXxRFJw7RToA2mGivc/1hXJRuGKrciEuKJaZjD6I6RvN97CJ8bP4UFRWgFfSYqMIpc9JciGTegnd47bn36+VUqMbAQrdljPE8LYl6PSIJ+xWy6bvFrP/8Q8JD5fi6yCgtc3D/na7EdNIQ3kzLwD4G/LxL+eqbMzzzVGtEp8iSl59j0nALE59vgVwOv27I5b2nH8FeVcrQ3joOHq2iUzs1kW3V5ObbkcsENGoZrkYoqVShVNmxOUREquu8BEGgRXM9vgFamrWp3gkaPdzxCm3NzgN5jLyuGeVFRZxJr+Sjr8rJKVaz/xisnXoK1+C2jHvwEfasXYe2Yi/LvopApZIhiiLffJfN2k8/5Kb/PX/eezudTtbPf59XnvTAZheJP+bk6w8Cyc2z4VDIiJkTxs137SEj+dQ/ungOpGVQmZ5GOGEoBAUiIjanDYWoxOB05UjcAfr1GPy3z1+I1hHt/pW74/d92yhMKcK7IhjBKWDEA53FyNsfvsKjs5/lwN5XKXUEope5IopOSsRCqlTl+CuCcYgOZMj+IvzVv4/OHbqyv+0e9h7cit7phiBAgZBNO48u+GoCqnvCnDhKl8hul/ReTYH4iLl0Qdqp1zdSc+MrYPvyVWz9+gO6dVIQFiJHIXdQWGilX/fqdMjikuozWIcNMnA0tpTVv+Ti6hdEiE85t9wUgFotQ6GQMWqEL5EtHSgVTpLP2LBYnOi01YLh7SnH11vOui1VyOWgVCkRBQVfLSmmd8/q3afDIbJ0RTEtug+qZd/Iex5kwTKBJ1/KZvEqO4+8BhaPAbyy+me6T3uJm17+mKnPv4ze1YWE39Zx2xQvVKrqj4QgCNw00Y/MxCNUVdQurwcoyslFIZbQOcqV43Fl9O+hRSYTcHNVYK0qR6WS0aurhtSEpIuuo1qpw4Edh+igwlaK1W7G4bDjcNrZsnMDdrv9vGdEUaSkrBiT+e97q5RXlHEq9SSlZcUX/PmhI/vRmVyRO+VoZXrUMg3ecn/UDi2btq9l+rSZnNIeJ4kjJMuPkeOaQofIKJLKjrEhfTk7czaQb86mwlZGuaykpr2uXCZn5m0P0CG6A1XqUvQuBnr6DSZAV90vXSEqMFvMF12XpobkU796SDv2y8RqsbD5y/d55yVPRg7SI5cLHD9hYfztWRyLN9GhvZ7ScgGrzUpuno2SMgdf/ght+vQm2PnLeeN16ajhwFELDqeTuCQbv+8zkZNnQ62WMW2SC2/NK+HHNRV06ixn534Hx447GSzYWfB1Gjv22hFc2zJpeO3Sde/AAO75YD7x+w6QW1BI/7siaNa2NYIgoHepXQRkt1jR/+VgYKVSQKEQcFjPPyRcqVZjMjlxOEQ83JWknqoWX4dTRPijj01GlgO/cNfznv0zew6cJqhrX9I2rMVo80CPEYWgpMxZgk1mpSK7kp37ttK/55CaZw4d28eCbz+mrKQMQS4Q1TGambc9gE5b7Q93Op0sW7WInbu2oZMZqHJWENOlO1Mm3F4rl1ytVpNnKcJDfi43/2z1aPKpEzx273N0jepJ/MljyGQyTp46wZFdh4kx9sdWYaPSVsG+3B0Y3Azcces9GPRGnE4np9NOUlZeSkxUT5Ljk4nQtkH+x5pYHGaKKaBNi/9WAHVe3HH6R0iifrWQhP0ySdx/iA6tBPr3MuAUReRA+1ZqJo428Oa8Yj5+XYtvaBDlZWbeXZBKYNQwJj/2CDlnUtn62QqcTrGmo6Moiuw5ZKPrDVOI3bCc7XuKSclwMGBcJuNGGNCoBQpKnOTZW6J1DqPTpOaMebE1cbv3caq4mB63tiS8Q7sLHm+nUCmJ7N3jvOt/JaxLb1auWccjs3U142z/rRCDVzAG9/N93K6eHniGtmHpD+lcP9yLO79PY/vvlYQGy1EZ3Fm3MZ+EFBUDHvnnFrQAr0+ZydulBezeXX0GqUO0Uykro4tPT2SCnN17f6sR9qTTCbz53stEONvTXOiEzWEleV8Sr5U+z8xpszl+4iiJyXGcPp5CtLEfSpkKu9NG/P5D/GxczriR54Sld4/+vLv7dXzFIFTCH2mJznRUCg0KZbWbxcXoSvfOvTFbTCxcvIAobR/UBg02gxWTuQq5VcC9jZGunXtSVFLIh/PfpLSgFC16ShyFGNyMHCn9HS8xACcO8oRMRgy7AQ+32l0oq0yV5BXk4O7miavx8tpAX6vMse5gzOhdDI1o39Cm/GeQhP0yMVdW4uenJTfPhtPpRCaAi1FOkL+Ck6dt3HxfIR07q0k+baZ5txFMvXMmcoWC0DatUHm147lXE5k6yQulUuCHlQXkVPgxffIEuo+4jt1rfiGncAkxHexk51morBKx20WoyEYhhzYxXQCIHjygzt6nz/hxfPvsXnKeTaV3Nw3JKTY2/+5kwpNP/u15qKPve5ilc15gw/YMvPy8uPORbHx8daj0IgpDAJP+9xhK1fk9Wi7EtEl3cepkMgFiEAq5El9tIEqZigJLbq1j4BYu+wI/Rwi+yurqUSUqWjg7sDdxMy+/8T98hWAKSvIoo5hcRSZB+jAUMiUtNO3ZvnMzY0dMqnmfTu2iCWsdzoH4bXiJ/lgwIcqdeOi86NC1Q633Li0rQYEKtbw64KlUqqr7z1hFcovPAPDlonnI89R01vVBEARsThtHS3bTc0AvqiqrUCqUTO5yE82bncuIcTqdrFr3A1u2bUAjaDE5q4ju3I2bJ05vEpWq8RFzpeyXBkAS9sskpFVLPv+4lPumeRISpESGSE6enYU/lBES2ZVb/vcUFSWl9A0MqJVVIggCNz7xNDt/WslTr2/C6XQQETOcW14Yh0KprL7XaWfyOFduHGanWYgKpVIgIcnMQ88VErfxe1rFdMc/LPQfrLt0dAYD0+e+w7Gdu9l6MgGDlx93vNUfo4c7KXEJHPx1JeWFufiEt6Pn6Btw9/XB1cuTO996n9SEE5QXl9DvvnDsNhsyuRxPf7+LHpD95zNOPd298fXzQ5WvqfFFi6JIlu0MQ2LOuZiysjIIkIXXGkcQBNRODc2ENgQbw9CWnkEQZMQXH8BH449KrkEt12KuMuF0OpDLFTXPPfXAS7zzyauknD6FUeaGqHDiGerOuOtrC5G7qwdOmR2TvbImBRKgyJpPSEgYRSUFpKaeoatuYM17K2VKguURpKWmcset93I07iCpGSkY9S74elefWrVz31Z2bt5BlLY3arkGu9NGwoHD/KT/nhtH33Kpv8ZrilcyDzFOcr80CJKwXyY5qam4uml59o1SJv2fvfMMjKLa+/Az2/um9x4IIdTQe5UuRQTEhr23V732evWK2K5dLCAWxAICikiR3jsBQihJSO9tk+xuts68H1aCMUFBohfvzfOJ7M6cOTvL/s6Zf52sR6WEpSutWGwqrnzkdkJjogmNiW7xXKVKxfArZjD8ihl4PR7K8gtx2OyN8eyFR/dx7c1qDAYRpdInEh2TNOg00KOTnGO797S6sAOo1Gp6jhwGI4c1vnZ423a2ffZvbrraRHyclp27t/DJE5uY9a9/ExgWikwm+922gL/F6YxTQRC44erbeWPuHCz2ClReDbXyaqLbRzeJivHz86emvpKQX6Sp2r02HDQQqPE5kzUaLV6HiJkAKpylROriKGsoJD42kaLSAn5Ys4zcvFMEB4UybtREHrvvOXIKsiktLyYkKJTE2KRmi5JKpWbc6EmsXbmKGEUSDR4rFc5SGtT1XD/iRmx2Ky6nm+qGSmQyGQaDEZVSjVKmoqAilyeffxCzGIhMkrFctpixoy9lwqjLWLdxNfHK5MYnAYVMSZQynvWbV3PpJVPQ6Qx/+N7+J1l+IJOp09eSEh72n57K/yRtwv4HKc/PZ+a0YBLi9WzcVI7XIzHu0kRi2zkoLyiiffeuvztG+o5drPv4HQJMburqPBgj2jPlvofRGM2UlVeT9PO6IHpF7HYPllo3chnwOzvh86EwK5vDmzbidtiI796Hzv37NCYViaLIpoUf8fKTYaR09CUwdU4xoVIVsW3JN0y++54LuvaufacI4IypJiYyjn898W8Opu/BUmshPjaR5MROTZKkpk6cydtzX0HtURMij8QlOckS09HLjeiUPhH0M/tT6izB6XVS57LglI5TqShi+oBrePWtF4jwxtFe3Z26gho+nPcu11x9A31SB5DwO31DxwyfiMvtYtGST8ApIAkg2CU++OxNVHIN9VYL1VIVRpkZm60Ef/9AisU8yqyl9DddgvHnSpEur4M1a1fSJaU7Vms9sT93ZXKJTqNuzGQAACAASURBVA5V7cbiqMIreXn42XuZMnE6lwxu3iv3YuZfRQcaRX1A8LD/9HT+J2kT9j9IQHgER9NErrkygAH9fKYWSZL49odcOnUO/d3zS3Ly2DD/VV5/LowOSQa8Xokvvi7mo4cfRC54eOzZUyTEyJk1w8y0S/W8/6mFpAQF368oYtjNrdONfu+atexZ8gEzJukwmxSsXL2d9E2duOLRJ5ArFNRWVqEQ60np2HTXNXSQP8v/eahV5nBnp6YONZ1Wx8Dew856/IBeQyi7vJhlKxdT4swHQSI8LhJnvROn14FarkGpVGEIMOC02TAkaImJiuXmwbfx7fdfEuGNI1rvi6vXKfRoXFqWfPclJqMZp8tJYmx7DL9o8vFL6q11rPxpGV6Xl0hZAmHyaDy4yTqajkfponfQMA5V7SJICkeNhsyqI5ijTIQJUY2iDr72e0FiOPsP7aFD+46UphcRp2/P4ao9yBwKutAPSeXFrPTnh++WExYcTufk7hd+s/8CZru2MHXIjjbzy3+YNmH/g3QZ2I8PlnzOoq9LuGxSyM/CXEqlPZik1N9vmHBw3VpmTtbRIcm3y5TLBVKSdbgXHOGZZzvQObkbR9OKef3Dama/WUlUuAqvKGfm9ChWfPcl/ceNvaC66HarlS2LPuSTt6MJD/eZAcaNkbj7H0c5unMPXQcPQGswYG+A+noPRuOZ/ypFRQ70/i1no/7ZCILA1AlXMnbEZErKCjGb/AgKCOHHdd+xas33+IvBiIJIrbyKe277R5MkoNy8bJLVPZuMJxcU5BWc4sP330Uj11AvWbhs0hXNuh5Z6mp47uXHaKhwEEo0EVIcbo8LncJAB1I56N6CSeXHgLBLKLTm4PA2oJTJGdBvCAc3HzjLp5GYNH46L2c+R0OdlYqGUroK/XELToL9Q9EotES6Eli/eS2dk7tTbalk2+5NlJeXEh/fjv49BzeGeF4sTOnYJuoXA20JSn8QtVbLNf+cw7oj7Rk/4yQTr8pmb0FnLrn+duz1zRN6fo29toKI8KYRI98sKeD+2/3p2d2AOTCADh39mfNUKEaDipkzE3n/3R7cdUcCkaEihVnZFzT/vIzjdE5WN4o6+BaXiWP0ZB/cCfjKJ7TvN5TX3y3E3uAFoLTUwXsLqkkdM7XJeB63m33rNvD1i8+y+JXZpO/c3SSa5df80nF6PuTkZ7Fw8Xw+/fIDSiuKG0MDx18ymacemc2wy4YTmxqNXq9n/ufv8eIbz3A86ygAgYHB1LvPlFeWJIk95ZuJlZLoqRtEJ01vuqkG8t13S8jObZpYtXbjSnRWExr0+BOMEhVqNNg9NuTI0UoGrO5a9AojHfy60jWgDxq1ji7J3akTarC6z7QBdolOKoVSUrv0JjwkgicefJ7YntEoFHI0ejVhoRGNDbC1ch11dRZO5WXy9OyH2bNmD9WHrWxYvp7nX30Cy1mSr/5qDh8vISNxDkpV217xYqBN2C+AgNAQZj72JA99voRBM66jIP0A2z95ng/vu4Hlb72By+k867mRHVPZsNXW5LW8PDtxMUqUGp+pRSaTkxCnxt9PwSUjgggL0yBJEjabF5X6wswxKo2G2npvs9frrF5UmjO7wHE33Uqx2Ieps04x6/Ycrru7mHbDZtFl4Jla6KIo8s3Lsynd/j6zxhQxfXA2h5e+yqr5835zDgGWcwuFPM2WXet5/e055O0qxHrMxfdfL+Pfc2fjdvt6sIYEhQICRw8cJcrenk7y3lRkVvLi60+zc/9Wxo2aSK73OHUunxiWNhTi8bqJM7dv9Fto5TpCxCi279nc5NoZxw4ToY1FLdNik+qRkJAhR5DAKTmwU4/d68uClSSJfHs2ASH+JCV05NqZN5Hu2cNxaxon6g9zsGEbIy8ZQ1y0zyQUFBDMDTPvIDA0CKVR2aSNX7m7iLDwCP750qNYKmrIt5yixFZAoiYFZbWWZSu/Pq97+Gew/EAmigmfEupv4rrkaf/p6bRBmymmVcjYvY9TmxfyydtRhIdrsDd4eeWNXaxZoGTi7Xe1eE6PEcP4ZP1KXnglj3GXmKmxuCkpE8ktVhIU6Wb3vnIEyU2w0Y4oCZhMPrPL6p8qccmCCU+Iu6A5x3fqyMpqLZu3VjF0cCAAZeVOvl5uY8L9IxqPU2k0TL3vAayWm6ivsRAYHtqkGTbAif0Hkdcd5fU345HLfQI5sL8/M29cRcXYCQRHNe/7+WvH6e/R4LCzeNkiuqr6oVP4zFfhUjRH8nezJ20HA3sPwyt6Wbl6GR3VqZQ1FHGq9gSBhOEvhjH3g9e55uqbmDHzGpb/8A0OmwOn6MCkN2M2+Te5llKmoqGhoclrBoMRR3UDSYGd2VW+Hq2kx58gXDgpJR+t3MBR6z5q5ZW4RAfGYCN33fAggiDQJ3UA7ROSOZi+F4/HQ+fkbk26OAEoFAquuPxavli0gDBXDFq5gSpPKQ6DlYOH9hHeEE+EPA4RkYKGbHYUryNGHs/6zd9jdRQwdextRIbHnPP9bC3aHKUXJ23C3gqkrV3GXTcFNJo1dFo5998VyeXXb2D0dTf+3PmoKWqtllnPvcSeVat57bOdqPVG+s0cy5y3PyTAUMzYEQYstV5WrKnD6dXy7JxCyiu9FFfpueLxx383Rvz3kMnlTHvoaV5++Z8sWpqL2SQjLd3JwBk3EpOc1Ox4g58Zg1/L5QHyjh5h1FB1o6gD6HUKBvbVknM0o0Vhh+aO098ipyAbHcZGUQefvT1IiODQkYMM7D0Mu92G0+EEFZyqPU4XWT9UghpRJhIkhvH9D9/y9CMv8tIzb2O11YMg8OTz92Pz1KNX+BymkiRRIRUzpMsVTa4/YugYPvtkPh1k3YimPcXkkMlhRETCZTEEE4krtI5rrroevVZPXHRik+/I3xzAiIFN7fa/pk/qAAIDgtm07Sdqqqvp06EPDY4Gjm7OQCMzggBy5ERIsViEEgzBdlIj9Vw7vZa35j3PP257E51Wd873tDWYOqRN1C9G2oS9FbDVVBMZ0fQHZTIp0WmgwWprUdgBtAY9Q6dfztDplwNQcDKLY2s1vP5sIHqNB7lCyaXjI/nHv+pRp9xIl6BALuvWpVmNc9HrpbywCK1ejzko8JznHZEYz93vzuNUegbOhgbuuD0FvanliJDfQmswU1rR3J5eViES3fm347DdHjf7D+/mxMkM/Pz86d9ryM8mlaboNDrckrOxlstpXJKTUIPPkavT6lCpVRTYThFAaGOZAK/kQafWEyCFkH7iICMGjsVk9C1SM6ZeyzdLFhLsiEAlaKikhIh2Ec0qL6Z27k3Z+BIWL1+ECzcS4C8EkaTohkqmpsiTg1KlpHOHM47zyupyLHU1RIRGnbOTMzG2PYmx7Rv//viL99DJjGi0OpwNDajQIJOBXmYg15HHA1OCGTcymN0HCth/eCeD+478jdFbl4zEOQgyWZuoX4S0CXsrENGhK1u27+PamDPifjSjHlFuxHSWWuYtcXT7Vq64zI92KWeSb0JiILWLC61eT1KP5iFvR3fuZt2CdzFqHNRbPQTGdmLyPQ9gDPBvdiz4dqRF2aeoKCwmODKCyHYJ5xRz/1t0GzqYjx9axCXD6uicYkKSJDZtqeJEjoJLHmrex/S049ThdPDvubOxFNYSQDA5Uh7rN67h1hvubhbeFxMZjzHASGFlDlG6eARBwO6xUSbkc2W/qwCQyxWMHT2Rr7/5HIPk+/we0Y0LB8GmUGo8ZciEpovioL7DiImKZcferdhtVkZ0Hk5qp97IZDKOZaZTbakkOiKWmMh4xo2cjEd0s/SbJSg9KtrLu6JERZ1YQyHZjE+ZBPjMRvMXvseJ48fQyvXYJStjRk7g0tFTz/tJKyE+icyDq0jx70GFtwybox7wUiGWMmOUPzMm+0JR28XLyDhedV5j/1EOHy9BMeFTlCpFm039IqVN2FuBgVOn8/lTu3A5C+nXx0xOXgPzFtYydNYDv9uB6JdIoohc0UIhL4WA6G3u6CzJyWP9vFd45ZlQUjpG4HaLfPpFLktenc31L7zcTEScDQ0seeVF7GUZdEnRsHqxA3VIR2Y8/PhZnyrOBb/gICbc/TgPP/9vwgKrcbpErB4/Zjz2XIu1YvLLLARYVGzN2kBdQT1d9X0b51rjDOPTLz9izjNvAZB+PI1jJ49iNBi5ctr1fLnkEw5Ub0WFGptQz7TLrmySWDRqyHicTgdffP0Jfp4gjCoTQX4heOUeLGIlXVOaLjRe0YvL7SK1Sy8SYtqhVKqw1NXwxvtzsFZYUYs6yhqKUOmVDBt0CSnJXdGbdJjcAaTbfJE/CpkClUHFmGETAfhiyQLKjlXQRz8cmSDH6XWw8ad1hIaG06f7gPO6t/16DmT9plVkVh4h3D8Wt93BSesRZk4LYM4zvp29JEls3+Oma3LC74zWOpx2lE6MHv+XXK+N86dN2FuBoIhwrpv9Bju/W866uUcxBLZn3H1TzjvVvkOffnz/0SomTfCg1/m+mvyCBvYfcnLnrc131Qd+WsXMybrGrFClUsaNsyJYsymb0tx8wuNjEUWR2opK1Hodm7/5mvaBJ3n8xURkMgFRlJjz7yzWLVzIhFtuuaB70KFXKu3eX0BhVjZyhYKIhPhmi9rSvemN/+4VGc2iJZ8SLkY0tUWrgzjVIFJQlMd3qxaTfzIffykENw5Wy3/g5uvuxGzyw+6wEx+diFbT1AQmCAKjhk6gpraKjVvWEawMwyKWU+uu4qorbiDAz2eqKirJ57Nv5pF2eD9a9Bh0RpQmBddffRvbdm5EVq6ms7oTZWXFmMRgchoy2LJmEzt2baVv/wHs3bWLGEUiXtFLraKSyybPINA/CHuDnYOH9tFHN7zx6UAt1xAtb8+GzWubCHtVTaWv2UdVJe0Sk+jdfUCzrkoatZaH732G1RtXkHZoP5oALdFCBF5qOHKsDqVSxpIV1VhqI+jyFyQxna6p3ibqFzdtwt5KBISGMOHWc+/P2RLxnVM40W0s192xmnEj1Njt8ONGByNvvBedsbnt226pJLpX07BHmUwgIkxFfU0NVaUlbPj0fZRSPVabSL2lnqVfpDSWC5bJBG65PoyrbvvpgoUd8FWvTG65l+dpUb8q6ucFKgq2JUZQt8eGxe7AT+cTNEmS8EgeMjIPU3SyiFTdwEbht7iq+fTLj3jp2bfPWvkwJz+Ltz98FbVTR4QqljJXER3bp/CPqx/H7+fol/LKMl566zmqyqroSE/8ZIE47Q4kvHz0yTs4XU66ywZSWlaM3CtHI9cSI7Unz3OCGE87SkuKeeTBp0nLOIBMEOje2RePDuBwNiCX5MiFpj8tjVxLVf2Z2P3MU8d558PX8HcHoxUMnDiwgnWbVvPwPU+j/1V9GKPBxPSJVzN94tXkFp7i66Wfs/z7fJYuryY4xMSIQZO49epJjcXN/izaGmX8fWgT9osIQRAYd9PNFAwexol9+1HoVFw3ZyABoSEtHh+e1I2N2442hisC1NS4OJbpJNHr5duXnyUlQaSs3EF9pRu73Y2tqghPUFxj1qper8DldDVzSrYWpwW9nTyQPuGRTd4bMGoUX6fPxc8djMXuE/U6CgmPCic7K5NQWVSTOfmpAnDXuVm4eB6hIeH06NqHsOAzETde0cvcj98gxp1EsM5XPbG92JlD2TspLMnHZDBzPPsoXy75BG+1hB8BBMp9jlotOux2K0aZPyWWI1jlVjxeN0pUuCQ3glyGV/ISro1hW84qwkIimNBCeKGfyR+D2UCNrYIA9ZnvrcxZSKdevkVNkiQ++2oecVJHQgy+eUYRz7HyNH7a/CNTxs1o8V6WV5by+rsvEulJZJh5IhV15ZwqPsrm7Zvp2W1gsxDKX2Opq2HLrtWUlB/DZAhjYO9xxETG/+Y5pzntKL2pc8tza+Pioi1B6SIksl0CiV27EBwZ+ZuJSL1GjSQty8zLr+dxOL2OTVsquffRfHqOn86Kd16nc4KDaeMVPHyHkdFD1USGK9m5p46q4sJGm/2KlWW0S+3V6qK+dG96k136r0UdoFuv/vS9dCSZqr0UqY6R7tpJgaqMW2bdjUqlwiM2bYmXUXOQKksVp3blsnvlHl54+Sk271zf+H5OfhaiHYI14Y2vKWUqQoli195tfPT528z74D0KswuRORUIXjmiKAIgCDIEZLgbPCgEJVYsyAQZIIAIJZ48wnRROEUHapUGmaypE/Y0MpmMq6ZfT6Z4hFzbCSocJZywHcJurGXsSJ9ztdpSRW21hWB10xo8EaoYDqbtO+s93bB1LYHucILkoZSXlyJ3KEiSulGcX8wLrz5JbuGps55bWV3B2wseISpyI/fcYmVw/3Q+//YZ0o+nnfWcxvv+8069TdT/PrTt2C8yKoqK+WbOs/hrLIQEK1n9voM+k69h8NTLmh2r0eu4/oVX2PnDSl78YCdqQyCp027GPzSUfUvn8u/nQ3A5PMTHqpgwSs/0m4v5ermVk6e8dO8OJ/IU7Dio4Opnb2q1+e/LLyS/zJe232h2OQuCIDDlqusZOmYCudknMZr8sDkMbMutpH/fISxI/4BQ0ddwo8ZVSWF9Dl0UfYjx8+0y7Z4ElixbRPfOPTEb/fB6fQ2mm19HTll5CdXFNaTqBpLu3I/b6qFCLMbhsaNTGZDwmYDK3IUk+3Un35qFVjSglrTYqcdBA10MvclqSGfw0OFnXQi9opeIsGjuu/Nhtu/ZTGVFBb3b92Zo/0saQyxVShVevIiIyDmzQLhEF5pfJX/9kqLiAkxyP2os1ajQoJT5HNNm/PF3h7J0xVc8cMfjLZ67ftsyZkyScf1M36LXs6uZ9glann1lPilJb57Vyd9mfvl70ibsFxGSJLHklX9x8zQ3E8f7Ihyqql3c8cBCwhPb0a5bl2bn6IxGRl45E66cidvlIn3HblbOX0BstAqbTcSklyEAgkxgwigDxzLd1DXomL/cQJ8Jl3HLNUNbtN//EU7v0HvXC3y/8DP+Lycb/8BgRk29nP7DRp1VDP0Dg/EPDG78W5DJyM6W8G/fmX1Zm/EjkBJ7If5CMOHBZ3b+OoUekyuAjJOH6d9zCAkx7XArndS6qjGrfGGmXslLuViAv+D/cw13X2ejfG82ZvzJkPYT7o5BEiQs6grkCoFQbSTh2mhO1h+hvKEYt8eFVtBxyLmdbt17MGXc9BY/x54DO1j83Rc47U5EQWRAn8Hcc9tDzfwBRoOJDkkdyT1xggR9RwRBwCO6KfBkMXVQy2MDxETHkZFzDKVLh1HuWyQ8khu7ZCVV15+0UzvOem5u4WHuv71py71unYx4PNnUWWsb/Q+/pE3U/760CftFRFH2KdRiBZeOOxO2Fhig4pppRn7cuKZFYT9NbVU1C595lPaRdYzr7mHPHjf3P1nGMw/6ExosRwJyC9xEx/pxIlfFgKlX0Hfs6FaZ9y/t6EH1dbz3wj+JowP99aOpr7fw/Yef4WxoYPj4yec0Xteu0XTtGo0gCFiTe1NVnke0I4qSg+VN6qgASIjIfzaLKJUqrr/6NuZ9+i7+1mAUkpIaeQUdu3VCqVBSUljB8dpDFFvz0KDFjhUHdmrFavQ6PWq1GjywrXwNclGBnywIo+SHRVZBbEoc99z0EEEBwS1NmWOZ6SxctIBkZXfMugBcXgeHdx5G4nOunnZjs+Ovm3kLb3/0KgdKt6IV9NRK1fTvP4j+PYc0HtPgsON0OTEb/RAEgeGDRrNj12bkaFGLGty4yJNOEqGPwSO5MRvP3jjcoDNTWmEnNvpMWGu9zYvTRWPBsV/yud839KRN1P+utAn7RYSrwYHZpGi2szWblXgctrOc5WPdp/OZMryBG2bFIYkSlw6Xs2ylhYVLbYSGKMjM8bB5p4tRo5QcytRx6x2DL3i+LZld5n4ynygpgTCDr0uInzqIjrIerFmymB79B7N3+3pKC45iCoigz+DxhEWc3eE3doqvxO7q5fuptURQdXgB0d4ENHJfiGOdqwarvLYxmWn3ge18/+O32B023EoXHZM7ccWIK0lK6MiJ7AzmHniT0toiQogiXuiIADglJ2lsw98RQs+gATjdTg5b92KljmghEVEQCVaEU2epxGxq3mRaFEVOZGfw8RdzCXSGY9T+bG6Ra0jWdWfnns1cNmFms1R/s8mfJx74FzkF2Vhqq4mJjCMowOdstTfY+GLJAtIO7UOGDL/AAK6efgPJ7Trxj3ue5K0PX2F37noMciOxpvZE6eLIcBxg/PiJZ72XvbuNZ+4nH5IYpyMoQIXTJfL2RyV0ShrYLMQyI3FOq4q63e5k3dpD5OaWEBMTxqgx3dDrz25yauPCaRP2i4io9oksLRDJzbMTF+sTAlGU+GFNHbHd+5/1PEmSOLl3B6/d76sWKMgEAsIjGTkUZt6Sz8FjSkpK7KiN/qTX9GXW87MuKCEJWghf/JmS/DzaqZs+WeiVJpy1Dcx96R6G9fNy7SQ9p3Kz+PSNNQwYfRNFubnUVdfQMTWVPoOHo9Y0ndvYKT1ZvXw/Md2GcvDQJvwIQhJE6oUabpx1Bzqtnj0Hd7Bo0ae0V3Sho7kX5bZiDu7ZzfGTGXRO7sb4UZMZNHQYXy5eQATxSIKEJIBdXo/R40eoFI1KrsFWbyNe3pEsjuAxOgnXRaNSxXK4YRcnso42yYj1eDzMXfA6OSdPUVldgUEKoqihkJDgUNQqnw1ciRKrrb7FGi6CILTYtenDz96m6qSFProRyAUFlZZS5n70Bo8++E8iw2OY/dQbLP3hK7Zs20CdUEWap5iRI8cwfODZn8B6dx9AdW0pV93+HTGRSopKncRF9WTGxOubHNfa5pfSUgsP3PMuHRNddOuk4si+g3z1xVpef+duwsNbzo5u48JpE/aLCJVGw8gb7uTuR95ixiQ9IUEK1my0UeqI45pf9CFtCUGQ4fWeqdeiVKswh0ZgDnEz/Z+vExITfV5ZsGfjl0lGLTlHw6KjsRytbGxTB2B3W3FTx6SRCgYN8MegVzC4fwAJMSXc89BsouiLVqFnfdpydqxby33PzkbzKyEcO6UnEQkhZMWmUFacSf/20XRJ7t4Y8/3D6mW0U3TCXx2EvcGGy+ImXkohvyaTwkMlPLP/YUYMHYNCoUQmCShkCmQyGQ6vDSN+jU9Jp8M+jZIfHrkblcoXlSRHgevn8sAAXq+HDdtXk3csn1TDINJ1+7DbrJgkfyqrKogMj8bqrkNQCvj7nXtZidKKYk5lZdFbP/znqBxflE+dzcKWHeu4YsosBARiY+KJi02gocFG/76DGdL/kt/8fgVBYOywyxnSdxxlFcX4mQPwNzed159hU/9o7g9cOlLklllR2O1Ohva3sujbah64dy4fzL8fk+nCNhhttEybsF9kpA4bSlhsHAc3/IQjt5aY/j0ZNXjgb3ZLEgSB5P5D+Pyrvdx1aySCICBJEl98XUanIZcQFtc6ja/Ptkv/JaOnTuf9o8+jtKsI0oZT77aQ6TiMSi2x6Mtyln1pxSW6iUuUc+9tasxaBeFCLA1eKwFSCKU5hezc9BPDxzW3x5+2va9ebqYU0OXX0zXZJ+zllaW00/sKcNVYqlELOnSCgXT3HoR6GSHEkLb+EDK5jCJ3DrEkIZcUKCQVFZQSpfNF2mi1OmxWK3VSDRqrliJrASglLJpKOiSmIEkSazb+wJp1K6ioqEDwysgniwRDMrvsG0ACjUdPUX0uRbIcpl5+RRPnqSRJ7D+yh+07N+Nxu+jZoy8Dew9t9B1UW6rQyYyNon4avcxIeUU5AAuXzOfQ7oNEyOPQE8ia5atJP3aYe25+6HcXb51WR3wLTwmzXVuYQuvb1HdsT+exhbHU1NioqbYQGCDnuhkGFiw6xd23v8k779/XJu5/Am3C/hdRUVRMaW4+AWEhRCTE/2bceHh8LOE33Xxe44+adQNfPJfNof/LpVuKkrSjbmq9EVz99KwLnfpvJhn9mnbJnbjpkYf5fuFnHMvbjzkgkN6XDGf1F1/QLTiVMJOvSFjWyTyef+kUTqfAPstGRLcXjWDAIlawYtFC+g29BK2u5YqIv7S9nzqQyZQe7QkPjaSmqpJAdTBerwetTE+VpxQZcrrI+yIAdtFGbEAiOyvX41G40HmM2FX1OCQrNepy/L0ByJQyioRcqj3lmKVAymVFlLjy0Sm1NDga2LV/G2tXrqKjphcxSht2r40cyzHkAQr6hY4gqy6Dkw0HSYnoyo3jb6fbr2rTfLXsM/bu2E2EEItcpmVVzg/sT9vD/932CHK5gsiwaKxiLW7R1RjOCGARKxiQOIii0gL27d1NL91Q5DLfzzdICiPt5HYyMo80qS55rnzu9w1TAk/9KY5SlUqBvcGLrc5CXLQSlUpGZZWH0BAlndvZ+X75Lq6ZNbzVr/u/Tpuw/8l43G6+e+dNitN30LWTll3ZTtRBHZj+0ONoDa3Xr1JvNnHzK29w8kAaZYXFdJsWRfvUrhdkfjmfmPRf0rFrDzq+3ANRFJHJZHw9fy4x6mScdWq8Bgm5TCDBFMOqk7m4kQh2BxItS8SLh5NeF+WFxdx/1TQ6dOnKmGlXENcuCZ2+efnf07b35QcymTR+Ggs+eR/JmYKIRLW3nEwxnTAhGpkgwyO6USgVhGgjiAlMoM+QvgjIiAiLJCkhhRVrvmVf2maQJOpUtXQz96XBY0MuqBmkH025s5i1m1Zy8NBektRdffXb9QIup4s4ksmpO0F0eAImlT9BphAmjruMlKSmvobyylJ27NhCL+1QFDLfLj5YCudQzg6OHE+je6demI1+DBsykh2bthOrSEIj11DsLMBtcjCo73DS0vdiloIaRR1AJsgwi0FkZh87b2FvbUfprxkxqhdzP97LjVcoUKlkSJLERwstjB4eyJABJhZ+d7xN2P8E2oT9T2b78u8xOfbw9mftUKlkiKLE6+/ksPaT+Uy++95WvZZMJiO5Vw+SezUvlXs+/FFBb2k+APU1Fkz6AOQu7RPgtAAAIABJREFUPdm5FjRqGS6XhMehwea1kqqKAyDDvR8tOnrLh4NDpGp3BXN23od/UBBd+/Rl5q13oTc0jbkfO6Unhw8XkHcKOgyeRPXJXWRbT+J2eDCoTRg8ZrySFycOAo0/l14QJHp06UO7uDN1bW686g5uvOoOikryee31F0nQJiNJEqLkRS5T4JU8ZGWdoK6uFoPRBIBeZ8DhaMBjc2NxVbOxfAU2Zz2x5vZ8+elCRO0C7r3tYaIjfKaw41npeB0iafZdqORqYgyJ+KkCMYvBHM/MoHunXgBMnXAl4aGRbNq6jjKblS69Uxk3chJGvRGD3ohbcDS7126ZE5OxedTOb/FXxKnffOtY7r8nh1n3HGfcCANHjjnR69X8+4VI1m604Odn+tOu/b9Mm7D/yRzd/COvPBGCSuUTOV/hrXCmztqE57Y7UCjPbjv/T3A+ZpdzJTk1lbUHlhDu1xeDyQ+3y4VCcKN0aFDWOLGLNhySDZfoIEXRE0kmYndZCVfE4BZcGL0myvYW8bH1Je55+l/Nxj9je4fI6I5M1OooLNzDilVLyK05gQ4DQf7B6HQGSuwFeFRuDPqWBcVk8scpOjhWc5BCWy5eyYNeaSJAE0hMhxhE0UtVTTlBGl+NmcCAYOzKegKVgdAgMDDwclRyn8O1pKGA9xe8yfOPvYrL7WTF6mV4bF5CZIE4JQf7bNvoENAFl+DAz3wmQkQmkzGwzzAG9hnWbH6dO3TjS/2nFNvzCNfEIAgClY4y6pTV9Orer9nxZ+O9o+kMS/zz49T1eg0fzP8/Zl31KvklFTxwVwI9uxsoKHLy6dd1PPLU75cxLi6uZvWP+6mttdI9tR2Dh6SgULRc0qENH60i7IIgjAXeBOTAPEmS5rTGuP8NOBsaMBqbiohOJ0cUvb56LReJsP8Zgn6a3oOGseOntRzN20uIIhKX10GxkMfka69j5/qfkJXIUXk1mC0BKORK6t0WVIIahUyJwWvE4Wmgi39f9p7YQElhPuFRLff2PL17zz1VTkJ0P95+aQLf/vAlW7duwI6F2ppqKm3lhJjDmP3SU0TFRHPzrLsbS/kCvl2xyUBZTjEpst5oZFqq3WUcd6UxPuVS+vcZzPwF7+G2OzGrAql2VVAszyE2Mh7yVY2iDhCmiSK38gSz33iK/MJclFYN8bKOqFATKFfjJwWRXr0bc7AffVLPrU67Uqnivtse5v0Fb5FZko7b6UZQw9Uzb8B8jjv22a4tTJm0g9GJ596a8EIQBIE33r6d55/9nGdeKSA4sIbCEpGbbp1C99SzFyErKanh++V7WbFsHZPHGmgXKmf5V/tZsTyKOa/ejEqlwGp1IJfL0GrPrzH6fzsXLOyCIMiBd4FRQCGwVxCE7yVJyrjQsf8biO/elx9X7+XG684k4mzYVEl4u+RmTaH/E7SW2eW3UKs13PfsC+zctI4je/ZgMvkzbtRMOnTqRnxSMu/Pfg6t3UC1WI6NOiRBQv6zDbqWakLUvkgfndxIbU1VE2Gvr7Ww4qvPSdu5A5lcTu+hwxg/7So2rz3GhmVbKcnYhyhJlLoKcTodDAoag78mCFESycs/ybvzXuPJB19odGbbG2xY662kGHrgsDuxS/XoFEaSNd04dvwot8y6mztvu58f137HybI0omKiuXfMQ6z6aQVOoWkzFEtdDTU11VjrrUiSlyBvFMjBI3fj9roQAJkg57JLZzRZXH6PwIAQlEolBrkJf0MwckHOd8u+RSFTMLT/Jb95bkbinD8l+uX3CAwy8sY7d1JQUEltrZ3ExLCzirHH4+W1l5ewY+sBKsprePGJQLp2VhIeEcCMKQL3P1nA/I9+4nhGNlkn85EQ6N0nhfsemEpAYOuUx/i70xo79j5AliRJpwAEQfgKmAy0CTswbObVfPZUGsXl+fRJ1XAs08XqzV5mPH7bf3pq5xS+2FqoNVqGjZ3IsLFNsyMTkjry2GtvsWvzerasWklxRS6R8njsdfWUePOwy61EGuNxe13Uey1ERMc1nut2uXjjmcdQlCrpqh+AiJejq/aTl5XJyIlT+GHBj7RTdCVG4Y/dU8FxVxpVznL8NUHIBBlxug7sL9tCXlEOcVG+Mg41tdWoZVpCAsKR/EVEUUIul2NxVVFYnM/ug9tRKpTcedMDTTI2e/UoZ+nxbwiVonA6HVRUllHntuDBTaQ7nhqpgjxO0lnqAzIICw1HlETMHj9SOpzf/d+5bwsNZU56mAY1LkjBnnC+/e4r+vYY1CyT9DSHj5eg+AvML7/HsYxCMk+WMGRICoFBzYX46y+3Ul1yhJeeCuXN9+1MGGWktNxFRXktYWH+TBln4P+e+IHnHo1mwovtcLlEPl6UxyP/+IgP5v9fq+Rr/N1pDWGPBAp+8Xch0PfXBwmCcCtwK/g6Dv2v4BccxM2vvs2BdRtZvvck5tBobnz5Eszn0Qu1tfm9JKO/Gv/AYMZNncmoSZez8cfv2bZ6Ffm2LAxyM50Ce1PrrCLPncngCeMw+Z2xRR/evwtPhZuO5p6Nr3U09uBA9laWfvYxnUzdCdZFUGuxIbnkxEsdOV5zAFWdFo1Gi5/ZH41Mh9Va13h+oH8wHpw4vHY0ch2n+4YX1+eTZ8nk+y+XI+KlQTGP22+8j+R2nQDo070/+w7sYl/GZmQWJQ5vA7VU00Hojh4jJgLI5QRlUiGBrlAstTVYtTVEREc2Nuk4V46kpxEsa9p5SqcwoPXoyC/KISmheeeu5QcySZr+LQadGo/Hy4b1R9i7OwOtTsPosb3o3Lll81ZrsmjhJhZ/uZoRg7Q4nRKfzPuO+x6YyYhLfP8Ha2vt5OaU893SzbzydBAKhYDd7iurHBykJDvHTmioHxWVDYQGCUwe73vKUSjk3HVTGLvuzOfggRx69kr80z/LxU5rCHtLAdnNWtZLkvQh8CFAQueU5i3t/4vRGQwMmnL2Oh5/JYu3H6DsSDqdJQ0R0fF4wzzIFReHD12hUDJq0uWMmnQ5dZYa1q1YSvrevegMBiaPvY6+g0c0Ob44Pw+9t2nhK0EQMGCmqCyH9kafYJj99Nis1cgkOZIEapkW0SlRWJ5PjbmC2KgzRdc0ag2jL7mUDat/IkHZEZ3CQLE1jyzbUQaEjGp0mtY4K5n78Ru89MzbaNQa5HIFd930IO9+8hppuw7ikOx0lvqgRYckgQcPIURwkkPUUIHH7iI+MpEHrmu5zO5vYTSZKBHLm7wmSRIO0YFe2zyE9l9FB5g6fS0p4WH08R/ME48uwFmfy8TRemrrvTz/1F5mXHUpl08feN5zOVcyT5awfMkavvwghsAAn5ktO7eBWx/8ih69Eln89VZWLN9MQoySwvxS5n5cz5ynE1GqFPyw1sqlow2+Riz1Hj75qoZ+vZv6rQRBoEM7JaUlNX/aZ/g70Rq/6EIg+hd/RwHFrTBuG63I0r3p2KuqKFj4If06iXRMkrFrp4cta8K58b7ZLcaJ/ycx+fkz9dqbmHpty7XiTxw9xNGD+yirKyJEFoneYGzMuLVRR1RcPFVFpUQa4hElEa/bhY16PLip81gQZR7yxExiQqIxGpqKxIRLpmA0mFj83UKqy6oRBAGTyh+z6szTgr86CF2DgYyTh+nRpQ/gi2ZRyzXEGdpxynISraRHFL0IggwZMjySGwVKIs2xeGRORo+ccF629dMM7j+ctw68SpAnDL3CiCRJ5NpPEhIZSkRYdJNjZ7u2MHXIjkbzy/p1h3HU5fLBazHI5b492SVDnVxz5w9cMjoVs7l5TZvWYNPGw1w6Stso6gCJcVr6pqp4+82VlOQdYsn8aPz9lOTla5k7v5TX3i3ghScTeeCJTL5aVofBoCAr30lsQgr1tsIm43s8EvvSHEyY/r9jDfgtWsMYtRdoLwhCvCAIKmAm8H0rjNtGK7Avv7DR9KLYspG7rlQw55lYZl0Zzbsvx9G3czkbVn79H57l+bFm+WLmv/ASilwVNk89J8rSKCnJx+Vxkmk9gjnKn5k330mhLJtC6ylsznpqxRqKFTlEmGMpURZQpagk3JCIQmgelSQIAtk5J9G6DfQNHE5nVR+UTg27yzfhlXwOUkmScDvcbN65ni271mNvsAOQ1D4Zu8yKXmWgVMhDEAS8khe7ZKWALGLN7UjxSwUV+Jv/WBGsdnEdmH75laR793DIsZO9DZsQIr3cccN9zTKap3Tc0cSmvmfXUS4drW8UdYDwUDXdUtQcSsv5Q/M5FyRRQtlChKIgg0MHM7jrhgD8/XzfRUS4P1dO9WPZDxUcPWFj6KAgTuZqSEodz3vzHualV2+gsNzIq+8Wk1fgIOOEjUefKyAhKYkOya0b0fV35YJ37JIkeQRBuBtYgy/c8WNJko5e8MzauGB+6Rx1Oh28lJXG5NlJje8LgsCMy4K569FNXDqj9boo/ZnUWqpZu3gxPXRDUMs1hOgjOV55kL3WjRgtZvqPGMVls27EYDRx17PPseqbLzmRlUaZopDuAQOINJ4Jrztemo5D0Twyo7iskIMH9tNbPwy5IMdBA9gF8t2ZlDYUEK6JIb8sh0J3HroME2syV7Hix6U8cPcT9Os5iI1b1qL3Gqj2llMqFSAh4RAa6BzQk3hjB/LtWSj95KS0P3t9/d9jcL8R9E4dQEFRLjqdnojQpv1hDx8vQTHhU5Sqpj9xnV6LxeL59XBYar3odGdvw3ihDB7Wmeee2Mz0KR7MJt+c8gsd7NrvRKfTEBJ85ntQKuWkpISj1VlYuz2IhHbRfLG4b5NqkK+/dSeffbqOe544jEqlZMSoYVx59dA/bf5/N1rFuCpJ0o/Aj60xVhsXTkvOUeFnV4hXbOre8Hqlv1UUQU7mcUyyANRyX+SHTmGgR9hggurCCBkSyTV3nMnmjUtM4o7HngFg6WfzObBqG37uIHQKAxUNxdSaSkntehXf7skgwOukd9d2GPRG8gpz8JMFIhd8W0yNWovBYEBbb+BU/QkqrWUUuXLpEtCLOKNvoSywZfPFNx/zj7uf5OF7n2H91tWkHdoPgkBUdDQ5p7KpqC6i3F5IbFw891/1GHL5hf38NGoN7ROSm71+2lEa6m9iYvT4Ju+NGdebpx/dzajhTiLDfUL+06ZqKi1qUnskNBur1mJjyeLtpO3PwGg2MGHiQAYOau6cram28tPaQ1hq6umWmkDvPu2a/L/q2DGKkWOHMu2mDYT4e6ixuCkogXvun0ZxYTlrNx3n1llnesAePGwjMjqcF1+5ucW6Sv4BBu67fwr33T/l3G/Y/xAXh9esjVbht5KMVGo18cm9WLz8JLNm+qIwRFFi4TcVdEyd8Ieut3/XVtYuWUxVeRlR8QlMmHkV7Tv+8V3ouaDTG3BJzVPqXTgx+p29g9Dkq69Hq9ezeeUK7LU2IuPiuWXW45QUFrDyq7dwWNx89qWTYQOGkNqtNw00bWwS4B9EkZBDYEIUObnZdNf3xyHa2VqyBlESCdGFU5ldTIPDjl5nYNKYaUwaM63xfEmSqLZUoZDLMbfQhq41SZr+LSnhYQwIHtbsveTkSK65fgqz7l5Opw4q6upFqmvVPP/iTcjlTRf4uroG7rnjbXp2dnD3dSbKK6v48J3Pyc8dzZXXnBn7UFouzz45jyF9FUSGyfh47laWLonn+dnXo/rFE8OYcb34bulW4mNlTJtoJqdA4tuv1nH/I1fx1msZ1NUXM7CPnsxTThYttfHQ49e3epP1/xUESfrrA1QSOqdI/1q66C+/7n8r55pkZKmuYsGbjxIVUkvHJDl709x4ZInMuuvZZs0tfo+dm9ex7IOPSVSkYFIFUOUoJZcT3P7k07RL7vSHP4soitRZatDqdC3Oyev18tx9t+NfE0yk3lcls95lIcO9jwdfeoWwyOgWRj2DJEl4vR4UCiWH9+/mi9feJEXTG73SSFWNhVPOdIzto5DZK1BV6ojVtUdARpmjiEJFFs8+9hIvv/lP6otseJweIoUEZMgplfIpleUz782vMOr/miSZels9x7PSkcvkpCR1RaPWkJE4B0Em46bOM37z3Lq6Bo4czkWrVdOte1wzUQf44vNNFGdt5NlHziTXlVe4uPL2QhZ+/TQmkxZRFLnmitk8ereG/r19C6vXK3Hf4/n0G3Ypl11+pszBc09/Tqf4PKaMM2Gpqcfj8bJtt5M12wOZ/fLNLFu6k5PHTxEaFkxqzyR+WrOHQwcyMZl1TJg0iKuvHd7iPP+X8Ddeu1+SpF6/d1zbjv1vzvkkGfkFBHLPU+9x7PABKivLGTghlnbJnc/bFCOKIiu//IJkVXdMal88fpg+BtEmseqbr7jloccoyM1GqzMQGRN3zruug7u3s+yTj2mosyEKIn2GDWfqrJtQqs7YX+VyOXc8/gzzXpnN/orNKAUVLoWDK++++3dFHXx+BcXP9dE3fL+cGHkSeqVPiAP9/TB6e7Mlaw3Dp9xB9YnN7M7egIBAUEgw9858CLPRj+SOnVmdtYI+ihHIfjbXRHkTccrtHDt5mD6pf17Y4Gm279nEV0s+xyj5IQkSdvlHjHnKn3hZ0O+KOoDJpG3RpPJL0g+fZNqYptFSIcEq2seryMosoUfPBLKzylApGujfO7TxGLlc4IopZhatONhE2PfvO85tV/hRVVlNaLAClUrOOL2K517LoKbGyi23jQF8tWHuvvV1br1Wx4sPx1NW4eL1uRt5o8LCgw9dfj636X+WNmH/m/JHk4wUCiVdejTLHzsvHA12bHX1mIxNk6wC1SHsSF/Dk7fdiA49Tq+DgKhgbnnocQKCQn5zzOwTGSx66x06KLvhZwjC5XVybP1BvvG8z9W3N62CGRQSytV33ktleSl+AYHEJXZoIv7nSm1VNbGKpCavqeRqjHo9UYkRmP2mc/O1obg9LvzNgY0LVFxUPAHqEBo8duSSAhEvMqWMSF08p3Kz/3RhL6so4avFn9NV1Q+dwie8ZdIJVrx8iO3bmzfO/qP4B/hRWFzR5DVRlCgscREQ4LuuXC7D45EaO0+dxuORmhXq0unUnMqtYVh/fWNRPLlchtkkY/HXm3n8qSsBWPbtdiaPVXH5xCAADHotc56KZMTU7ej1BuLiQxg6rNN514fJyixhzep9WOtt9OydwrDhnf5ri4n9bz/X/A3ZfOgIHy1ahr2qmquiuv5HMkc1Wh0anRarq7bJ6wXWbFx1Lroq+tFV259e+mHIC5TMe/VFfmnyqygrYcU3n7PwvTfZs20jbpeLDT98R6QUh5/a92NWydV0MHTnwLZt2OrPZIZmHD7AU3fcxLzn5vDt3Hl8+f67VJSdX9qEJEkcTduHw2lnX+lmCq2nECVfhmOtswq1UUP/gV2ISAhh/ckytpyqaSJaQQEhqE1KQkLCMPmbCAoKJiIsCpe8gaCgoPO+n+fLvkO7CBDDGkXdoS7FrDUTIYtmx/bjLZ5TVmbhg/d+5LGHPuSdt76noKDyd69z6eT+LPzWyoksXyin2y0yd0EZ4ZHRxMX7Fur4hBCUGj/WbrQ0nudwiCxcUsvQEU0tBn0HdOPjRbWIvluNxyPx1rxqRg0LIDszv/G4vJxCUrucMcO5XCJP/OsUZr0dmX0bO9cv57qrXiTnVJnv+Nxy1qxOY/++bMTTg/+KH1fu55EH3sJfmUZq+xx+WPINjz00H7e7eYTQfwNtO/a/CXty8lj77ieU70nDT+6PlwY+7rOHa+64D5X6zwtTawmZTMboqdNY+/kSkuiGQWmmxllBljWdRH3nxn6ngiAQa0hiX+EmigvyiIyJ49jhA3z8yssEeELRCjpObjvM5pUrcDndhKqimlxHKVOhEtTU1VrQG01UV5az4NWXSRK6468LQpIkSqvyee+Ff/LM2x+iPMdKmd8t+oRdK9cTLEag8mjILD1CgTaLKFMixUIOM2+7C5lM9otywL5mHgk6A12Tw+mQmII+SE9RxSlidUnIkFFsz8emrqVv6qBWv9+/xuV2IZd8ezKHuhSAIK0/Ja58nA53s+Nzc8p58L53GDtMwfSxWtKPl3HfHXt4/qXb6NSpZfNVWZmFbZvTMZkDuOLmHOJitDQ4JNolJfDMc1c1HicIAk88fS2PPvQBq9bbiAyTsWWXg+69ujNmbPcmY15/40hGf76GS68pJCFWye4Ddgx6BXExEgrVmfo2UTERHM443Giz/2ppOQq5i3fnhBCfGIFSIee7H6t4Zc6XxMaFs3fXQXqnasnJc+MSzcx59VZCQs440u12Jx+8u5R5/44gLsZ3ncnjArnrkQLW/XSYceMvrH/BxUibsP8N2JdfyNZvVuDcncMI81gUMiVeycux3Qf4IWAhU2f99THow8ZNQiaXs3bpEqy1tQSFhpEQ1BFjWfMUf5WgwW6rx+v18sV7b9Ne1pUAk2/HFyklkJG7D1WshkpXKX7qM5mYdrcVr9xLUIgvDG7/ji34uYPxNwU1jh2uj6XCWszxIwfOycRUUVbC1pWr6aUfilL2/+ydZ3wU5dqHr5ntPbvpPSGEXqQJIr1IkSaCCoLCESv2rliwiwVFj4pdAbGgoii9I0gJICW0hCQkJKS37XXm/bDHQA6giA3Py/XBnxmnPPO4859n7qomaAhQX1fDT86NBFK93HjtIzRt0bic7ala8d1504PM/fw9tuasAgQS4hO586oHTspi/TNo17ID69esIVZlRomSKL0VT8BDPVV0vajZSft/8O4Srh2rYdzl4TnveRGkJdfwzpvfMuuNW0/a/3BuKQ/c8yaD+6i48Wotu7KtLFrh5dEnr6Nr10wgXIFxxbJdbNywE1Gh4LobRqBUKqirc/HQoASytuVw/eQXUSmV9BvYldFjLiIiwsjV11zCvp9+JPuQnWvGWriwo5asn7x8u+IomzYe5OIeLbjs8ou54+YskuKruaSvlSWrqph8pR6T2YDqP6aT4YNtvPDv/RAoZ+FH6Wi14e5MH86v4MXnPuPFV44X2du7p5BmTZQNog7hvggjBhlZu3nveWE/z1/LFxt3cGzHTupz8nHuOUBbdbeGlmoKQUFTfWt+XLWCURMm/+Wx6IIg0HvQMHpdcinBYBClUsnGVUtZ9eFCYuXjyTKugAOv6CIlvSnlpcX47F7qpGqK6g+jVxlJNjclTpVMTbCSOl0V+fb9xGgTcQedFIZyuPTa8Q32c6fdjlo+/nXiCjio9Vbi9Xtx1Nedcpz/Td6hfVjFqIZ+ogqFEq1OT5w/BZ3JSHL6yY2ef+bEVnyjOmZy+w3343I7CYaCZ1wL/Y+gSWom6UNC7F6VRboik2pPBVWKY9x8z8BGK9Wf2bkjh0dva7wyH9jHylMv5xAIBHHYPSxbupPS0ipatEhl9aosbpigZ/Sw8Au0b08rSQmVfPPVerp2zUSSJKY/OhdP/WGuGGEmEJSZ/+VXNGnRgdvvHMVtN79OZnIdj91pxeeTeP+TZRzYV8DjT03klluHMXHcfgb3CTJ8kBFRVPCvibH06B7kuX9/zUXdHyQ5OYqnX7iRd99axNOv5GKv96FSRxIb1zhM1OP2Mu6yONRqgfp6N6GQxJjhEcz/uoDaGifW//gB9HoNdkfjksoA9fYQOv3/ZiPt88J+jvL5+iy2z3qLyFo18UIMO6vtVAllaOL1GPThH6xaocXv8iFJob8tyUgQhAYTyIU9+7JlzSqyC7cRLSbgkzyUCUWMum4yGq0Or9tNRWUJIiIWInFhZ1P9UhL06ahiFNz55LOsWvQ1h/ftI8JmY/yIW2nf+SIqy0vxuJykN29B1tL1pEiZ5NTsptiej4VI7HINiz6ZS1pmCxKSUn9xvDq9ET8+ALwBN/uObscZqscruSj7sYhnj93GnU88izUy+pTH/9zM45uduQCM6pj5B87mmXGg6QwGZGTwwKSurFuzD7VaycBBw06bTm8266io8hNhOf64V9cE0OrU5OaW8uiD79Krq0hydIilX29k7cZ6pt3SuHfqpQNtzHonB4Ad2/MpL85lzhupKJXhF3iviyyMnbKDT2MiMetqeOy+5IaX+8wWBi6fvJ/cnFIym8WjECXGX5lGSqIGhVJEADpdIOOwV1BT7SQq2kyrVsm88vpUJEli/rwNLFm9mj49aSg5uHhFDSFJSUyMgoL8UnTacM+a2hoJp8OJ2+PDSvg5ad0mGbfPwLLVNQzuH3b4V1b5+ewbB/dN6/JH/W85pzgv7OcYP0e7uDfuJLpGR0tTR/Lr9iPLErmhveQX76dZbHvSzS0ocxXRpFmLhvC9vxuNVsft059l2w9r2bd9O9aIJEb3/xfpmeHsyC1rV5GgSCNRaoJG1GALxaD2a8n17yHqSCwzH72foVeOY+jYcdiiYqivq2HW9Ic5mpuPRtQQVAYwxJjZWrQST52HVmIXZEEiM6ItHq+TD16ewbSZb/xieGWrdh35XO+n2JHPoYpdGEJm4kjBKdRT663AX+Ll6zkfcN1dD5z2HKezvf8V/Nyn9Pr24QiSjp1Ozhb9b4YO78Fr76zmhceT0OsV+HwSr8wuZ/ClF/HvV75i6mQtrZsG0esExg638djzQWa+kcezjzVHrw9/2VTVBDCawqaMXT/l0b+HtkHUAXQ6Bb27adm4YR/D+mga/T9Qq0Uu6qzj4IFiMpvFY4kwcvSom8gIGZVKiU6vxuEM4Q+A3hD+InO7fXzw3nJWLtuGx+1HBi6flMclffQUFElk54gMG9Wbjz7ZxLMP2zCbws/A/kNOvN4Ae/cUkZgYNuuJosj0pyfzyIPv8cW3RUTaFOzY4+XqiUN+sYPTP5nzwn6O8N9JRjMOfkC8OoUj9kOU1B6hg7InoWAQv+SjoPIAVd4yiJCZOumJv3nkjdFotPQcMISeA4ac9N+yt2fRLLYdzio7Lr8DKShhwIQoiii8Kmprq/n8hdl8JM5EbzFgMJmJ9MRyobkfoiDi9NeTXbENXYwJqycajVqNwWRGpzcQIUdyrPIIJUVHSEo9/rDKskxhfi72ulqS05pgjYzm5ocrUBcyAAAgAElEQVQf54WH7kYd1JAgpCELMlZlNHGkcMC1gz3btiBJ0q9+BZ1oe+dg6Z8u7mfbfHrc1b0pL69hxMQsmmVoOFzgo2PnNoy5oifLF2+kU1sbamWQmKiwiI+/3MKN95ZSXFJDZmYcfp/Ea+9UMPjScBin2WygNO9k00ZphURsnI28wiocDg/2ehchScJg0JFb4KdrPwsej5+qKjfPzCzi1adi8fol1vzgZvFKJ4UlMo8+9AHX3zyCD99bik1/lLlvxGE2Klm4pJr3PnHiFrvRpXc0901vzcEDJdx83SoenVFNz656CooCrFzv4ebrEtiwdjuDh3RoGFtG0zjmff4wP+3Mx+n0cseDaQ2mGoBQSGLrllzKy+to1jyBVq2S/tFZr+eF/RzgVElGOoMBv+TjSN1BMoV2GEQTkiqEM1hPmqYFx3QFPPrCWw2OxX8CGq0OKSARl5RMVVkZfpcPjaiFICi8Ii3ljgiygEpUk1O7m7Lqo6QamiNYwg+YUW0h3pdKqbuQeFsKUYbjQioIAgpBQTB4PCqkvraat194hpqjlegEPfZQLRcPHsSoCZNJb9IcZ6UDrahDKaoREFChRhlSEZKCv+mh/tn2/rNj9c9gbsQXdOLsuh8pFCL33Hc510waQFFhFQmJNuLjrTidXkIS1NV5yGxy/KtPoxFRqxWMmVxErx4BcvL8dO7ajmsnh9vuDRjYnuvmLWXQTw46dwiXDV63sY79ufDv2YMZN2Yb6YluRgwyIssCH39eQtZOL8+/msJH76+kQysvKYnxTLr9GA5ngN7ddfTroWXHngDe+lxuv/lVIswCs+Y1bahCOWFsDEeKAkRYdAy9NOzs1Bs0tGwZRb9eJvYddBITpWPOW+nsO+giO+/kjHqFQqRzl5N9KOXlddx312ysRheZTZR8/ZmP5PSmTH/qmkYlEf5J/DNH/T/CLyUZXTxoMAsOvI0n6EavMAEyAdmPzmDEFhtDmbvwHyXqABcPGsT6T76njapreJUuKDgq5yHLEolCBkE5iBotSkFJstiUeqmGoD+Az+tBqw3XCdcpDJjMEZRXH23kpK31ViFr5Uar9XlvvoZcCJ2MvRAEgYDkJ2vpepIzMjBZrXh0boLeICrCK1VJDuGRnHTrOvA3r9ZOdKzCH2t735/x/FmL+olER1uIjj7uXDUatXTo1IKvvs/m3lsi0KhF/H6ZD+bX868JiXzwqZOho6/gjmYJJCQcT0aLjDLx6BP/4vFn5mE11xIISgRCBp554QbUahUKlYLFq3zM+9JJKCTTPNNAz64WVq3YzeqV23jnpWhio9V8sbCcR++2MKjvz81BlEy5u4zEWJkom7JRaWGAdq00bD90PGehWbN4/EEdUZFKHrwzFbc7RHmln/lf1zNwWOOmLL/EzBcXcGnfIP+6OtxFKhiUeeDJAr747AcmXNP3LGb67+e8sP9N/FopgI5de1A8Ip+FH39IeagYkxiBUq0kMiaGSk8pyen/vPZffQePoLSwiKwf16BWaimXionQRaGWtSCDAgUIIIgiGllLiBBBKUDA728Q9spgKd0vuYScvXv4ad9GrKFoAoKPWlUlk269r8HfYK+rJW/ffroaBzSItEpUk6zMYNPyZQy+4ire3/E8WsmAFJAQZZGj8mHUVi3jrp96Vvd3omnmj7K9n8r8UlRYSXFxDelNYhqVsj0b7rpnDJMm5rNxawndO+vZscdHm1Ymqmtk+vS9gN592pzyuE6dM/j0y0c5eLAEhUKkWbN4RFFk4w8H6N7ZxMtPJlBZFUCpFLBZVSxdVcP6HTn4/UH0OgW79jpJjFfQvrUGrUbA65PRakXGDjfy2bcedme7wpVHFQJ1tS7q652sWFNJaa2a0tJa4uOtiKLIA9Mm8Oi0d9EqiimrcKNSCri9Krr39ZzR/dvtHvbtOczMR477KpRKgUlXRfL0rK3nhf08Z8YvVWA8EUEQGDn+WhJSU5j7yiz0ohGbMZoyz1GOCnncePW0v2rIfwjFhQWs+vYrSgqO0KRNSzLbtiU7axuVuaUoA2qqA+VYsKFR6hAQqJTKiNTHctC3k8xAW3weDxXBEpTxCnr0H0y/oaPYv3s7Odl7MZrNdOnRp1HZAp/Pi1JQIv5XcrVK1FDvrqZFmwsYNmkC38+fh+AVcfsdxDVJ4ZFH38Ro+n2x6H+U7f2/Rd3t9vH0E5+QezCXzCYa9h/y0r1nR+6+7/KzTo2PjDKx8LvHeeC+j/hm+V4uaGdhXw7EJ8Xx+FOjf/FYhUI8KcEpwmqgpCxsDjuxxnrxsQDWSAvdurfhy+9y6djWgFIh4PfL+APHS0dLEvj8MkqtlcdnHGXscB0qhYct273k5gcYcomDO255jdnv3Y0t0kT7C9Lo268zRw79yKvPZJAQb6CyRua+x5dii7QwYOAvZ2bLkoQoctLXgUYjEAyeIkSyzsXBgyXYbCaaZsads3b488L+F3GmFRj/my4X9yXCFsWKrxaQW7yHhMxUbhnzOE0yf7mA07lEQe5B3nzqCeKCycRrUqkrr2LF/gVMefAh/D4vG1ctZ/ePm3G7HJikCFyyA4dYR6Ixjfo4LdYWUbgdTlpmdKS6rJwnbrsRS4SNPsNHMHridad8uCKjY9FbTdTYy4nUxSHJEoIgUOYvomO3cHZo70HD6Na7PyVHj2AyRxAd+8c6P3+P7f1Z/wZG0Xil/tYb32PTFbJobjpKpYDHE+K+6Xv4/NMYrp7Y56zHqVQqefmVKZSV1ZF3uIzYWAtNM89uLlq3TkahtvH2x2W0bq7DaFSiEOGrxW5emtUVg1HLnVP/TV5BPQdyfezPCaBQCiTFa3C5QnzwaR3ZhyQ0mhAfz3cx51MZQQCVUmT0iGhuujYev7+MbxZu4V9TBuL3B1mzKotPZ6c1vEgSVCEmX6llxoufotEo6X5xi9NWhbREGEhNS2Th91UkJWjQ60Vat9DzxTc1XNzzeMKbLMvMm7OGBZ+tomWmmpLSABZbHE88PYmo6D8/Ke23cr5s75/M2Qr6n0VtdRU+n5eYuITfFfvudjkpKzmKOcL6q7b+WdMfRsgVSTCmNWwrcx3Fm+Li3mdfAqC6spwV337JtrVrcDrt6HUGWlzQkbH/uoHa6kqOHslj8fz5JITSiNUl4w46yPcfoM8Vwxl82amrGebs28ObT01HU6tHFVRTJ1ShjFHyxJvvY4n4c2uin8iyb3Y0/PuZCPzciC/oFJnfSNSDwRAjhz7C1x8mY7Med3QeyHEz7Xkncz87d77g5s1dy+szvyAzXcDhlCgulbn/oYlcOT78QnW5vKxcvpuNP+xlx7Z9dO2oRK8JsHK9C7tLQY+ueioqPdw00UifHjoO5gR49rVajHoFGRlmBvS28eVyC8++cAP1dS6uGfckK7/MQBAEnE4vZaXVeDwSNz1QTmbTaAyWVJ5+fvJpHaHvv7eSN1/9gnat1Xg8EkePSaQ2SWP2e3diNocTmH7YsJ8PZs/jzRlJREWqkCSZ9+dVsH1/JDNfu/kvm9vzZXvPAc7U7PJXUFdTzZzXZ1J46DBKUYnGrGX8LbfRos0Fv37wCciyzIpvv2TlV1+iEwx4Qi4y2rbi2tvuOW1D7CO5h+iqH9BoW4w+kY15ixvCCiOjYxk3ZSrjpkzF63EjCAL2+jrefOYJfNUe/PVeat1VRFij0Bi1aJU6dEojqxZ+Td8hw09Zu91oNiMIAqJWBAES1em4JDuLP5/H+Btv+033/Xv4b9v7L4n76RylwWCIUPB4W7mfibQqcblObjzyRyDLMiUlNUghieSUqDMyOxw6WMLCz5fx8euJmI0BZBmydoeY/+UaxlzZHYVCxGDQMmp0V0aN7ord7mH92mxcLh8XDjAw/8MF+P0u7rjeQrdOGiwmkc4XKHjkLisvvlHP5m312KxaYuPDc2gy6zCaTOzZ76JdKwPlZTUkJSj5brmTnt2sPPtoCrc/VMSyJTsZMerCk8abc+gYSxetYcmCNljNEn5/kA2bPcxdKGE0Hs9yXrr4R64bbyEqMvxSFUWByeNj+Orq/Aab/7nEeWH/EziXBB3CD+js559EUaykq7E/oiBS46rg/RdmcP+LM3+TCWJX1o+s+fwbLtD3QKvUIckSubv38Ok7b5w2qcccYcXlcmDRHI+scAccGC0RiKJI2bFi1ny3kMLcw8QmJtK1X38qjhWzaP5cjG4z7SIvorL2GAliOofqdmFUR5BsaoJOqUf0COzYupFWbTsSYYtsdN21ixeRKKSTFne8fVxQCpD1w1qGj7sGk/n0HZd+K4FAgKVffcqPK1fg83po2b4DIyZMIi7heGGzE7NWT+VY/WZnLs0yTh39otWqyWyWzMp1tQ3ZkwBLVtbQqfPJ7fFORzAYYtPGg+QdLiMh0Uafvq3Rak8uf1uQX87zz3xCXU0lClFArTVz70PjadMm5RfPv/i7rfS7OERKYohIqwYZmQhLkHkLKti5M48uXRq/1MxmHcNHhrM/16zeS7MmGtZvrCaziRmNWiTsTZdp11LD0WMB4mNVLFjk5O0PugPh5KMpN45k2rPzmTLegNXoY+uOEHMWOHhtRnNEUeDyYSa+XrnrlMK+bGkWY4cbaJKqb9g2dlQEi1cXseunIw0JYA67i0hr40RApVIgwqLE6fDCX5OfdsacF/Y/kHPN7PIzRw4fov5YDR3/E/YHYNPGEGmPZfPaVYy4auIZn2vDksWkKDPRKsMrZFEQyTC2Ydv21bgcdgyncDz2GzGK5R99QStFZ7RKPb6gh1zvXvqNHklJ0RFee+xhonzxxGgSOVZ4hBmL7yTF1BSd3YBTtLPFuxIxoKAuUIUKNbvKfsATcODyOyl3lrDk3U/5Sn6Xjj17cOWUmxsiY8qOFmNWNa4ZrxRV6EQDtVUVZy3sB/bsZPFn8yk9WkhUbDyDx17JTz9upHDbYVrpOqPWayjZVcCsnId56KVZmE8w+5yYtXqi7f3Nfdn0Gfs9reJPb9a6+fbLeOT+t8nN99OqmYbtuz2s3wKvvnFyMtipsNs93HPHWxg1tVx4gYpNq0LM+WAJL792S6MVp88X4IF73ubGCWqGDUpHEGD9pnoee+hdPpz7IJYIw2mvkZtTQp9OArENjlOBuBgVtgjYvu1kYT+RppnxvPGqh9QkLXkFASwmEb1egRSEnXu8WC0iB3NDXHfTAF58bj6FheVYLGZatsmk3yV9+HrFQXbvPMqoS23ccVMqtbVBXK4QXp+M+jT1+p0OF62STnY8R1oVOJ3HI2s6dWnDklUb6NzheHes/Ydc1DsUpKVHEwgEkWXOmbj3c2MU/wP8lk5GfzX2+jq0guGkT2mtqKeu6tfrcp+Is95OnOL4qk2WZUQElCjxeNynFPaeA4ficjpYu+hbFH4lQSFI75GX0u/SUbz38nPE+pNJMWciyzJ7KrbQnPYYvRHIChm9YOSAZwcOuZb2QncEBDy4yK/eT0AO0M06kGhDPEEpyP71WSyPXMClY8NlZVObZXK4YD827QnRMiEvXtlN1Bl8pRTm57JswWcUHj5MVGwsAy8fg0ql5v0ZM2gitqKLth/1FdXMefkVAkE/PaMubWiAnWpqhtfhZvO6lQwadbIP4MS49/1tShk14kcuyWhDiuHU4YUArVol88a797Bo4WaWbiwnPSOFdz7o2iiD8pf48P3ltMu088Adx+u4fPxZOa+/+jW33nEZW7fkotGokCSJjJQQI4Yc/wLq0yOCdZtcrFq5h8vHXnTaaxhNZlaudxEdpWR3tpcIi0C3TjoOHvbR8eLjv78d2/NYu3ongUCQHr3ac3GPFqSkRNGxS3sO7NnOzNn13DDRRLdOGg4fCfDsrHq8fh2DhnVmzYq13HOTjW+XBjmcX4TkKONAsY6aaj2R0bFs3lZNTq6DCIuCnDw/ElrumzbslOPtfGErln57gOGDZUQxPL6KSj+79vm4e1paw36jx3Tnjqk7efjpYvr3NFBc6ufThS4mTB7JM0/MZ8uP4ee/Y5fm3HrHZY3i/v8Ozgv77+RsOxn9laSkN8Uu1RCQ/KhENaFQkFAoRK1cQdc2vxyn6/N52bN9C5VlpSSkpNKsfTtyl2djVlupq6nCabfjkuxUqcspO1Z8SkeqKIoMvXwc/S8dRV1tNRHWyAabeP6BA7TRXojTX4/dX4cv4CZakYAr6EBQiISkIFY5Gh8eNCotrqATUVAQG0qiQnmMqKjw9ZSikgxdGzYuX9og7H2GDCdr/TqOOFTE65NxB1wU+PfTe+Sw0/oDfqYwP5d/T3+MxGAaLXWdcBTWMufFV1CbNaSLLYnRh01skbo4Yn3JlLjyUUQ3XvmZRRvF+QWnvcbPtvfmmke4rs2ZJR/Fx1u58ZahZ7Tvf7Np/S5mvxjZ6AV/5ahoXuj/Ewf25tK7uw6nS2bxyjquGBGeH1kGu92N0+HGYvSwZctBRo/pdlp7e49eLXnswfWAzIhBBoqPBZl0eymyLLBy+TaumdSH+fPWsXrpaob2UyKKId54eQvffduWGS9dxwMPX8EXn8XzyZyVPPxcFV5fEJ1WRUpqPLffO5IFn67i0buiyS/0gORn0ZxkfD6J8mrYma3gsefKuGWSgeGXGFCpBHLy/Dz8nIPISCMLPt/I1wvWUllZT8uWKUyacil9+7Vh2eIt3PZgEcMHmai3h/h0oYPxE4YQYT3+ZWI26/j37NtY/N0OFm84hDUygqdndGHGs5/Sv7uPJ75ogigKLFhUxj23v8kHc+//zR2e/kjOC/tZ8k8Q9J+xRkbRc8gQNn+/CrPDiuSTqJLLcGrr0RtP33i5urKc16ZPgzoRfdDAZuVKVNFq/BYfO4rXY/CYCYpByoWjZBha8/FLL3HbU0+TcprStxqtjtj4xs009EYjW3NXEwoGEVHiCjhxinZkUcJssuCsryckhxAEAS8e1HoN0XFxFBw5BIrG7di0Ch1up7Ph76iYOO588jkWf/4Je7I3Y7JGMGTYOC7uN+hX52zpF5+SGEwnyRS2seqUejQ+HRvyv6d1cuOKgLH6JA5W7SQkBVGIxx8pu1RLZtqvN/ZWKv+aypyCIPDfDYb27neiVfuZ/04yUbawEHXrpOHZmUe459ZEaqrrkYJeLGaRzVlO6l3ZvPLyQu6+99Qx7vZ6F726G3nsbjMqlYBCFOjQTsMd06po19zHB++uZNnidcx8XEdqsgq9TmTkID1jpmTx1ZetGDO2O+Mn9CE6OoK331yIyRCiti5I85bJdO2ayQvPzqPTBc14d04JN11rQakUUChE/Md8DOobwdMvBhg5PAWzIdz4vFtXDTdOquaF5xdg0tby4mMxNEmNYeOWep594j2eeO5mnn9pCqtW7GbV5r3oDTrum3bhKYuDGQxarrjqYq64KlwzZ+MPB7AaHdw06fgX7MQrYtidXczaNdkNpQ/+Ds4L+1lwLptdTseI8dey/ccNFNcVoNXoiDMkY9G0Z96sV4md8VIjJ9/PfPnBuxhrIkg3h51zsiyTU76bJt0y2FK6kpAuhF5tpIu5LxaNjUJHLmsXL+LaW+8+ozHJsozX68bgN5OmaI4HFzvYQLlUTCLpuOudiAqRckUxZmUEEdGR6PVGBEHArbKjFhp3jjrmKqRZm7aNtsUnpTDlnod+83wV5eXRUtep0TaLxoZCUFDtKydeeYI5CgmNSct+5w4y9K1Ri1qOuQpw6uq4qN/A33ztP4ve/Tox5/MsHrknseGF+MpbxVw9xtog6gAjh0bx8hvHmHL7YS4boiQlSc2sd+uIjdHxwWMZXHlDFvmXXUyTjNiTrrEjK5uJV8bj9bkJBGRkZFo309KquZYuHXTMnptF+5YSTdPVDRE+ep2CcZeZeP+dJYwZ2529ewt5583PefWpOJo31eP3S7zxQTHPPPkJiYmR7DsUzkr9OSfL45VQqZWEQhJKpYCAgMl0/LdhMSk4fKiAZQtaEh8b3t63p5U6e4jPP1nNU89NZuiwTgwd1umk+/kliouradns5MqqrZopOFZS/ZvO9UdzXth/A+datMtv4djRIwTsfvqljGq0ynU46vlx9QpGT2zcBDkQCHBg104uMl7SsE0QBJJ1TdmzYzNR5ng66ns2OsasiqCy5Mz7j5YWFxJ0BWkeeQH2ulpyArtJpzkVHMOFHYNspspfSmRaLJI3RJVUislnpSZQgSvKgUJWctiRjUVhoz5UQ62mkn+NvA+fz4tGo/31AfwCUbGxOApr0SmPR0u4A05MVguFoRw0Xi1WTTR2fy05vj2Mu/k26qur2LRyBT53OCrmjonPYon4e22tJ3Ltvwbw4L0FXHtrEZ3bqzmQG+RQvsig/ic7Q7t0srE/T8f784+SnKih98VRjB0ZjVot0vsiLTt35p9S2HU6LV6fE5VKJDVJgSzJIMi4XEHKy+tQKk2UlfswGRubrbw+mbLSWmRZ5rtvfuTaK0w0bxqee7Va5LYpcQyfkM/IMYN55pUVXNDawGffOGjeVEVZRQhbpJVN29zYnQJwPDdHlmW+WWonOkrTIOo/0761gU8Wlp71fGZkxPHeMj+SdNw+L8syWbsDDB3999ZxOi/sZ8C5Gu3yW6ivq0UnnuxA1YsGaioqTtpfEML/kGmcwCYjoVZr8Pt9eINutCcIX7W/nKbNW53xmNwuJxpRh8VqQ2XUECraSZKiKXFSKnZ1NVqLngQhDbuphkmP3sO6xYsoLy4hPbMZXRP6UFpUREXZMfySF6sxCmdePe88+yyyKNOlV29GXzvlrAV+4OVjmPPiK2h8OiwaG+6Ak0OeXQy+YhyxCYl8P38e2eXbiLBFMuKaiXTve8l/ykBMQpblczLV3GDQMuuNqWRtO0x+XjkjOtgYO1HFW7M+YuyIEHp9WGyPFHnZtS/A6LG9sZf5eOjOhEbnKa+UaNpeSzAYOqmUwcDB3Zj38Wfcd5OSuvog0ZEKPvvGRUy0knUbXShVNn7KDrA5y83FXcMvlNx8H9+vcJOcFLb/11TXktKrsX1aqRRIiFXTvn0aEZZRzP1oKbk5Ln7a62PYICtlVTXs2CMzacpwbrp3E+NHm4i0KVi80kVIjEcWyigt9zUS9937XKSknn2cYqfOTZijj+Xpl0u49qoolEqB+V9VU+u00LPX35sZfl7Yf4V/otnlVCSnNsERqsMf8qFWHP9x10gV9G034qT9lUoV7bpcyJFth2hqDkdqyLJMoTuHC4f3RaPTs2HB96SpmqNTGanwFFOnr6Lv0JFnPKbElCZ4BBfuoBOlqPxPdxyJEEHizCnodAbK6o9ir6tBo9EycepdOB12Zk1/mH2rd2AKReBROHGo6gl4fbRUd8JmjCEg+clZu5dPvf9m0u33ntV8te1wIWNvvoHv5s/FVe9ApVHRZ+xIBo0aiyiKdOzWI+wXUChOEvFzRdS9Xj8rlu1i908HMVlMDBl6Ic1bJNK1WzO6dgv3RpVlmR87d2H8TdsY2l9HbV2QhUvs9Op/ET17teKeO1YzqJ+DFk3VuJxeNmU5Wbm2mq07PuXl5+fTsUtzpt4+iuTkcHPx1NRoElJbMnHqBjq0UVJeGaS2XsJgUHJJ30hWrK9l+GW9uf6edXTrrEOnFdmf48Nk0jN4ZNiR37ptM9Zu3NjQzBqgtNzHkeIgGU3jaNc+jWEjuuD1+tm5I58D+4tp1cnMrfe3w2zW0at3G5Yt2Yprv5tufVpzyeAOfDZ/PQ89vZYHb48hI03Lxi31vD3HzhPPXX3W8yuKIs+/dD0ff7CKqQ/uICRJ9OjdgZmzBqJS/b3Ser6kwGn4JzlHz5RFn83hx29XkqzMCNuBfUfwWdzc9MCjpDQ5Ob64vq6Gfz/1GJ5yF3rJhEOoIyYjgZsfegyNVse2jWtZu+hbHPX1ZLZpw5CxV53kHP01NqxcwvcfzSVOTuVI3SGMPjPx2lR0egM11ZUUyPsJqPwYbWZGTLyGmqpKDi3bTXPz8YzZfcXbqZCK6ZsyqmFbSAqyzbWGx954C4s18lSXPiMkScLjdqHV6lAo/5yH9ajhMa5tMeYPPafH4+eeO97CZqzmkj4GKioDfPati+tvvoJBJzSggLC4Z+8t4uUXviY3p4Bhl5gRFUp+zPIz/LJ+fPTed0RF+NCoBUrLA/j8MvdMjWfE4GhWrHMy56sAj0y/ltmvf4OjvpIIi4JNm8sZPiiCC9qYsNmUtG9jJDFewyVj83h/7jTmzVnDN19tINKqwOsXGTS0O1NvG45CIVJb42TqjbPo0TnAJf0slJX7eXdePUNGDOaq8b3Oaj5kWebLLzax8Mu1VFQcj4rp1PmfVSX1TEsKnBf2/+J/wexyOmRZZufWjWxavozioiM4amqx6qMJ4Ce+STLX3f3ASSIYCoU4uPcnqirKiE9KoWmLNn94f9XDB7PZtHI5VeXl5B3KxlvnIRgIIiFhFCzYFDFUi2VorTo0ei2thQsxqI5H85SWFPGTZyOXNBnb0OwbYJdnE9dPf5jUJpkcPZLHiq8XUJyfT0xiEgMvu5ymLX49YuWv4M8Q9gWfb2Lf9uW8MP14zfr8Ix5uur+Sz756DI2msdNvx/Y8XnvpfT58LRmTMfwCyz7gYvIdxTRJUXHvLVEEg35sZidKUeD6eyt4/bk41CqRGa9XsWS1m4duj+JfE1MwGDRszargzmmFfPhGK1pkhk0uy1bXMP9bNW+9dxcAtbVO8vPKSW8Si81mJOfQMd6d/R0/7cwFWcTnD+Bxu9HrNQwa2oOHHhnzt/X2PVc4XyvmLPhfMbucDkEQ6NStJ1ZbFG898STdrAMxqMIdcAryDvL+zBnc/dQLjY5RKBS0vuD478hhrycYCDSk7zvs9ezcspEdG9YjyzKdevWiR/8hDQ2uf42aqgrKSo6S2bYtMYmJlOcVEyMno0GHS6inghLiFMloJC3l9qNIooRP6UUj6FD+ZwWt1qqRPCGEE0r0eoMevLKbmLhEjhw+xBtPTic+mEKqpjhpR3gAACAASURBVDn12TW8ve8pJt17b6N7+19i+7ZsRg3Q/ydSJGwHb5KmIzEuXM+lXfu0RvuvW7uL0UMNDaIOkJGmgpCD7h2NxEZ6cbv9mIwKYqIUtGquYvsuF/176OnZVcveAz4mXWWk9FgVKamxdO4YxWWX1nHLffnceG0shwuC/LAtxDMvTADgu2+3MfejpXg9blRqDQMGdWPFkk1MnWzk7uuSuenug0wZb6BHt1RCsp6Zb+3m3dkGbrzl0l+9d0mSyNp2mGPHamnaNI42bVPOGfPYX8V5Yed/0+zyS2xauYx4OaVh1SsIAunGFmzLX0NZyVHiEpNPOqaupppP3nqNvH37EQURrTnsNC05UoDObyDN0AKTJYI1Hy1k3/Ysbnn4iV9dXX0z/0NWf/0N0Yp4ZAXkVeyje9QgXLIdrWAgQohClgWOBQtIUTYj37cf0SmS7d5KmtgSjVZLVGws1YoK1EYNR5yHiNMl4Q66KAzk0H/saHR6Pd99Oo+kUBMSTeHYZKPagsaj49u5H9OqfScEQcDn85K1cR3ZWVkYLWa6D7jkH1Ua+UTWrdnLxg37aJuuJi3Bg06nJS7ehiAI1NaF0Os1Jx0Tdvge/9vj8VNSXIlWLaPTCkRaJVyuIA4nxMcokCTQqARiohVs/clDbIwSi1mFzx+grs5FTIyFNq0i+emQmsPl6cSlR/LeLR2x2oysXL6LLz/9hpenx9IiM4HCo17umraSpHgFI4emMWNWIeNHm/jX+AgOF/hokm7jxemJjLluI+Mm9GuouHgqaqod3H/PO6jFWmwRMh++46VJ0wyee3HKKROGvF4/LqcPq83wP/U18L9zJ2fB9qLiRqv0/w+iDmCvrUOr0DfaJggCWlGHy+k4aX9Jknjjmcdx7XPQ1TiAjupeKHNVHMstRBvQ0055EUq3irzyfZRVF7Nt7Tqeu+92SouLTnl9h72OR6ZO5uv3PiClLhNTrRWz24oRC54aN4JCgYwEyEQQiVO245FcBKUA7Uzd0RkM5JFNgfsA64oXISTDwy+/TlKfdPI0+3Am1nH5rdc1lPMtPJxLjK5xeGqkNpaykmKCwSA+n5fXnpjGive/JJgdpPyHY7w1/Uk2rVn+x0z4GbCoaPEpGzv8Vg4eLOHfr87nvttiWLnOg9mkQK0KcKykii++qcJoiSKj6cmheL16t2fhUhdud3gM1dV26u0hgiGBxas9+P2QGK8kFIKV690cyPHTtImK9z6p53B+AKdTpqo6iE4rEAgEwlVA17kZeVkP7rz7Mq66uldD6YMFn6/i/qmRtMgM/wZTk7Xce4uF/HwXsiyTV+CmYzstoiig1Yj4/UGsESoS45QcO1bzi/f/+qvf0CKtDo/LTnmZnczUIFlbfuLhB+Y02s/nC/DKy18zdtTjXD/pGSZe9Rzr12af5qz/PP7frtj/yTHpv5dWnTqyYf8SYjh+3+6AE6/obtQz9GfyDu3DVe6gg7EHgiBQb68hSozHGbLjkV2ICgUF0kF0IT0dVD0JCH7qDlYx/dYbuHjgIHpeMoT0zHCSU2V5KY9PnYK72kmMnIQoKxBREHT68cs+kGU0Oi0ByQ8hATd2RFlBbmg3Jl24qmOSMZ06XxV2fy1FwVyuumkqaU2bk9a0+Snv12qLxFFTh01xvGaMO+jAYDSiUCjYumE1jiP1tDV2bfhkjw7E883HH9Lpop5odfpTnvePQJZlPp+TiLR3Aa/Z15KUFMU1k4fSb8DZLTIWLdzEhDFGrh4TQygoc/XNpbRuruanvV4iolN48ZVJpzRLdLmwKZu7XMhVN2xlYG8tObk17Dvk59lHMygo9HLVjaVc2EFJbr6f3PwAGo3A+JvKuHKUiScfjCY7R8WN95YzfJAOm83IrHeLqXFG0rtvW/buLUSrUZPRNJZ92UfJOVRCUkIqMv8JhAIy0g3U1gcJBGRSknTsO+jjgjZavD4JtVqJ0xWipCxAXGwEkiRhr/dgMGoaRZ/4/UE2b9pDpMXL9RNMDLsknMx2MNfLFddncTj30oYGIrNmLsRXn82X7ydjjVCye5+Lh5/5BKvtxpPMVP9E/t8J+7km6LIss3PLD/8p9+ql/UUX0Wvg0FPWF/+j6NZ7IJtXr2JfSRbRygS8IQ9lQiEjJl97yusW5ucSsgepqD+GWqPB7/OhQIkeE/VU45DrCMoBUmiGAgUeKYDZZ8XhrWPXsh/ZsGQxGp2WqJg43B4nlppIbIoYpFC4yJcn5Eal0GIRbByRDtJK3wWFQkGVo5Qj0iGUBhW2mBgSPGlA+OvCqo3Gqo3G4a7D6aj/xfsdcNloFs7+EI1Ch0Flwht0k+PZQ9+rRiKKItlZWcSIiY0ET68yogsaKCo4TLNWf96X3I9rl6I59B3PPG6jc8sYdu118vgLn6JSK+nZ68xzAn6mprqW1K5hU8s1V8UxYkgUe/c7KS6v5s4HriIx8dQJU4IgcMddoxg0pAvbtuSSnbuWu27S0renlb49YfAAG3M/O8KubAffzknGZlXw0ps1lFUEqa2TuXRQFEcK/bw820HXbql079mB7hYDkyc8R2yUjMMZoqDQRXysBpPez5r1x7i4q4nk5GiUSgWFJQL+gMh3y6sZMTiKux/JQa2WubirjcrqIC+8Xk7vfl3YuSOP999ZhNPhAEHJ0GEXc90Ng1AqFUiShNPpp2kKDB903LneJE3NFSMMLFuaxa2ZI6ivc7Fxw04WzUnDYAj7Hy5oY+T6CV6++mL9eWH/J3GuRrt8Nec9diz/gUQxHZNoZdP8Feza/CN3PvH8GTsgf0aSJIryc3G7XaRlNDttoSudXs9dT85g87oV7N+xk8iIaEYMnEjTFq3xeT3kHshGEASatmxDRWkJi+fPx+6qI0XMxOf14JO9KFHhFOsJyRLloSIMsglBAJ/kBRkMSjOGoJkidw7JYiZGvxmLxsbuss2oZQ0pQiYH5Z3EkYxaUBOQ/UQrEsnT7OWAuIMAfjxqF3qjka59+hOflMzGz5aTIKc1CLA/5MMh1522Ns3PXNizH26nk2VffoHskpAV0Gf0MAYMD9c7MUZYKJdKGh0jyzJ+yferxcJ+D5Ik8eOq+Tz5eByZTf0IgkCHdibuv1Xi/U9WnpWwt2zdlPU//kCPbuEY8AiLkhaZemrtNWQ0/fVknBYtEmnRIpG09Gjefn0eTdJ0tGlpIBSS2ZejQG8wcrRMQ2SUjglXGrhz2lGeeT2I0VhBx85tWfDtIJRKkfy8MmY8/RGznomjVXMDFRX1LF0ZZOESDzffbGPW7HJEQaKs3IfLZ2TWO/Xcee841mbl8srsXBAsvDlXwcx3q9Bo7ETHRLL34A4+m7ecUUNtTLs7DadL4umZm3nnrRC33DYcrVZNSloCavVRNm51U1DoJzVZRdM0JYmJenJLXQBUVTmIjlQ2iPrPNE3XsXD531sK4I/i/4Wwn6vRLtUV5WxesZIuhr4NYXo2bQx7jmxmV9YmunTvc8bnqig7xjsznsZV6UQtaHBhZ+Q119Jz4KkrAer0evoNHUW/ocdjv/fu3Mbc115BFzIiyzI+lRuT1Uoqzag2l5Pr3EuS0ISQHKQwdAi3wkn7qG4cqNmJJ+QiTkhGFBRolFoEBMqkIqKEeJIVGXgkF2pJQ4bYmv2h7XgDbqxEs0fejE2OJSD4cOudTLrrXgoP57J71WbambqhUeoo+eEIuRF70MTpOFC6gzh1Cv6Qj2Ipn94jhv1q2r4gCPQdOpIeA4fiqK/FZI5AdUJ97u79B/LWD08SHYhHrwrfe5Ezl4ikSBJTTjZN/RqSJLFvVxa7t25BpVLTuWdvMk6RkRvw+/C46klLswL+hu1tWho4erT4N18XYNRl3bj5+i289MYxBvW1UFHp5915dYy9asAvOh3/m169W+NxX8G057/HYS9FqVJz2ZiBjBhr451PV1JcdAyzRc8VV49k/IQ+iKJIZWU9zz01nyP5hQQDXtwuL3X1UQA4nW7GjbawdpMLtRjkxcdjeXdeLY+9UI1er+fZF2+ib7+2QJ9G4/B6/dx565s0T62l38VqjPpYFi5xcde0w8ye2Zzp98dzxfWbmTxlEDqdmtvuHMmUa2fgdIXocoGW1T84qLPLmEwWRo0LO8MTEm1U1cgcK/OREHfckbx5u4PMcyQE9vfyu4RdEIQXgeGEf5V5wGRZluv+iIH9EZzr0S5H8nOIECMbxV4LgoCNGHL3Zp+xsEuSxLsvPIOxwkJzQ4dwkaygk0UfzSUxLf2Mojvq62r4+NWZtBI7YtaHhbLWW8X63YsYmj6BBEMaRZpcSuwFeLwu6uRqrJoodlb/gNlqJTo6nrqKKqJCCfjsXspCR/ELXqxiNMFgkKAcIBgMIooKdBiQBIlUoRlRUgIH2IFX4eKZmR8RYYvm6/fep4sl/LILSUEsShs15RV0HtsHrU7Hni1b0RkMXDnoJi7o0v2M51ulUmGLijlpe5PMloy6bhILP/oQfdCAX/IRkRTJ9fc9/JvD5GRZZu4br3Bw825iSCBEiB1rNtB3zCiGjL6y0b5qjRaDOZLcXBdd2h/fvnO3g/QmCZwNlggD/559O1989gMz3tqP2Wxm4pSh9O7z2wVr0JAODBzUHqfTx5GCct58bSGlx8oIhuCii9tw171jGhyisizz8P3vMaC7hzeebkJVVR05uXU89WI+s2e2RJZlkCXiokVkQaBTez1tW+roP6aIdm1t+HzBU45he1YeaqGaaXcnc6SgjKR4Fe3u1XHNraVs3WHnoi4W9Dqoq3Oh06nZtDGba66MYeIYNZFWBaJC4OU3a1mzWcZg0LBm9V46dEhn3NWXcPejy7ltio3kRA3rNtXz9ZIAs97sfVbzfq7xe1fsK4GHZFkOCoIwA3gIOHV/tL+Yc3WVfiJmSwRe2X3Sdg9urFFnni159Egezgo7zQwXNAiRXmkkTk5my5pVZyTse7ZvwRK0YTYdX/1atVGoRDV19ipirAmkWZpj8JhwBOwUqA7QP+kyQlKQPc4tDBw1GqfdwYYlizlmLyRJ34R4KRW7sxYjFiQk3HVOApIfO7VoRB0BfNSIlRhVZkzWCKJiEygqOIxJGYFSVFHtKWNX+Y9oJQOyLPHdJ/O4/oGHuO/5l894bs6Ui/sNotNFPSkqOIzeYCQxJf2sYp9zD+zlwJaf6GDs2dB0IyGUyqqvvqJrr76NXiyCINBr8DU89eLLTL9HR9c2RrbvcvDSW7Xc9/BlZ30vtkgTN00dCpx53XZZlsnadpg1q3YQCobo3rMdvfu0RhRFPB4fj097j/unmunXMwOvV+KdOUU8Nu1DXnvzVgRBIDu7CClQw+TxqQiCgF6vJT1FzcghBhYtreKKkUaOHLWTtcvH3TeH5+DbZXbatTLStrmS777dymszv6SqshpBEGnZOpXrbhhO3uEyunZUIQgCGq0al9uPzaqia0cNuXkeYqPVBIJKoqLC+Rgrlm7jvZetGI0K3L5whM8VoyKZu6CAt197h5REI6+8GGD8xMFcde2VvP/5Oqqr62jdtimvvD6ApKSzz1I+l/hdwi7L8ooT/twC/LHpc2fBueYc/SUymrdGG23gSPkhUoyZiIJItaecWmUlF/bqd8bn8XrcqAT1SUKkFjW4Hc7THNUYn9eLQjr552DRRVLgO4BNikZExOV2UMoRUixhu7ZCVJKiasrmVat5YMYrDL18HFvWr2bBe+/gOFaHW3aiQEGMMgmFqKQ4kIdOYyAmIgFf0EMzXTti9Ylsd63D63FjjYzCFXJQ5S5jc8kKbMSSoExDI+sQ1LDg7XdIz2xBTNzZrWh/RpIkSooKCIVCJKWmo1Sq0Or0v9tRuv+nHdhCMQ2iDqBWaIkgipz9e+jWq3FT7y4X9+OHumKee+cz/FWFZGTEc+9Dk7mw6+lbyP0ZvPv2Mjat+4ErRxrRqAW+/OQAG9e35JHp41ny/XYG9VEzoHe4fZ5er+COG+MYe90R9u8vpnXrZGprnCQnqBp+gyaTlvo6NTqNi+27HKQkaZj5ZiU+n8zKdQ4OHg6wKzvArOebce0tuRiNNRg0AZ59KYrUJCWbt9fy2ssfc1GP7tSWhQXaZjNTfLQCQQiQfchPi8wgDzxVxoRrw6UIXnhuAVUVNaiVOpQKEZdLQpYFfF4/kVaBt1+IoK5eRqGycuejy3nwsRt5ffYdf+k8/1X8kTb2fwGf/4Hn+02c62aXUyGKIlOnTeejWS+xNW81SlGJ1qxjytQHiYw+uSTq6UhtkolbcOEKOBqSjmRZpkI6xtAu487oHM3btGe5uICgFGgwDQUkPwqjguad25G1Yy1a9BRL+aRZmpMRcfzTXimq8Xt9DX93692futoq5r3+GlYxmiq5jKJgLoTAqo5GEGh0fI23Ap3FQGR0LKIoojap2ZqzmjgpBbWgJc+fjV5hoktEH5yeenZs/oEhlzU2a/wWigsL+GDmDNzVThSCAlkH19x2Jy3advj1g38FrUFPUDjZrBAUAqcNm4xrfwEjJx9jePLZdUb6vRw9WsWy79ez4P1UzKawJAzub+OaW/ezc0c+ZaWVdG3VOLlHEAQy0zWUl9XRunUyLVom8fLzXuyOIGaTEkEQSEiKZMvOOkprItm4K5F7pw3n9VcX8vmiGgb1szBjrJUPPqmg3hEgMV7DvbdEcWGHsB9AoxExmmQ++uoQHreKeQsquGJkFFHRUbw/7yhrN/pwBSK4ZsoA+vVvy6aNBzl8YDdXjo5m2RrX/7V33+FRVekDx79n+mTSeyEkIYSEIh1DRwURUNHF7toFFHEta9d1beuuq/7UVWwIduxiAUGqFKWF3gJSpCSk98wkmXLP749BQkyAkESGhPN5Hh8zkzv3vneYvHPvKe/hb+PDCLC5+O1ADT8utjNmRACR4Sb8bR6ycyu56hJ/5s9b2yZGwDTkhIldCLEQaKi48GNSyu8Ob/MY4AZmHGc/E4GJAOGxLbukd2todjmW0PBI/v7M8xQV5OFyOomMiTvpGXAWqx+X3XIrM995l8iqOMw6CwXyEGEpUfQd2Lg2w/jEZNJHnkfG/CVEEAdSUqA7xJCLxnDptTdRVlpMYV4uM954lbDiaHSiNsacmv30GFC7DqamaSyZNYs00ZMoYztA4NHc7NG2ISwCl9FJZsU6QnSRODwVZMvfuOy6Cd6ZkUUFOMrspOl7YdRMmLAQTjSZ2joq3KXopB5ndVUDZ9A4LqeTN599kmhHAp39vLNOS6oLmP7Cf3nsldePlEpoqj4DhrLwq2+odJbhb/KOTCmsyqXGXEXn7r5bUed41q/by5B065GkDt4a6KPOsZCxeiedUhNZuXYHF4+qfU1NjcaGrVXceIf3bzkyMohRFw5m8kMrueWaIIKDDHz/Yxkljkje//hvR2a7pvdP4cP3FjF38Vrm/lRGQnIyA9MFmzbn06d77cWMzSro3NHAwf0FvP/JI7z68te889EupISefVKZPX9cnQW4V/y8hUtH+zF8aAh33L+THbtzOauzgZ9+drBxaw3LZ3k7wf2senTCjcWso9pR/We+rT51wsQupRxxvN8LIW4ELgKGy+NUFJNSTgWmgrcI2EnG2aDW1OxyIidzhd6QAeecT1z7RFYuXkhleTnp/a6jd/8hJzVk8rIbx9O9XzrrV/7srS0+8AY6pnlL9hYX5LN07mykJtlUsZIYZwLB+lBKRRGmWDPDL6odXZOfkw01EGQOw+mqwaQzo9cZiCCWHTUbuOvxZynKz2Pt8qUc2L0LP0MAP3wwg+Vzf6DX4EFYHFbMWPEIFzqhQy/MhGpR7CvaiSfYTddeTa/vsn3zegwOIzG22hWQQiwRBFeEs3bFUkZc1PCSb40VERXDNZMn89lbr+Pn8EdDw2NxM/GBx5q9+MefxWazUFyq1Xu+qFTDFmHlgtG9+PbrJbz81iEuHR1CeaWHqR8U0a9/T+Ljw49sf/vkC1kwvx1fzF2Jw17F2QOG8sp9A+uUMLDZLEy680Im3emt+XLoUDF3TnyedrFmtmR6JyUBVFVL9ux3Ed8+nLi4UP774gQcjhqEEA2WBtAb9DhdEBZq5KO3urDkl1LWbSigsEjD5qenutqbcqSUOJ0asxc4GHvlWfX201Y0d1TMKLydpcOkbKAX8E/y+5j0tpDQW1L7DikNlt9tLCEEnbp2p1PXunc+m9au4uNXXiFGSyTWmIjRbCZb+4349A707zOC3v0H10laJrMZt3QRHhVFQW4uDnclAh12rZyY5Pb07DeA8tIS5n35Od39+hNmjUZKSU7+fuZ9/SWaQyNB54fb4KTKbceIERc15FYdZNTYq0hObfqQNIe9EqOsXyvFqJmpLC9v8n6P1mfAELr27MOendvRGwwkp3Y96TkJp9LAQam88apkZUYZA/oFUV3tYs3afKZ/lE1Kp18ICrLxypTJfPzRYu59YitWPzMjR43isivqjkgSQjDygp6MvKC2pLLT6SYrq4jQUP8Ga9TExobSvWcXsvZu5qkXC3nqwTAS2hlYta6KqZ94uGlCbX3/hl7/u+EjevP8vzK48PwQQoKNjDw3FKtF46Mvynj07+257YEc/jLGH4Ne8s3cGtp37MG553VrgXfv9NTcNvYpgBlYcLjTZJWU8vZmR3UMp+sko7ZM0zRmvjuNTvoehNgiAO8VrqHcgNVmY8Cw+jd0oeGRxHVMImvXPhLapVBZVkZZRTF5nix6pQzEXlHOupXL8LMHYDHbcDprMJnMxNoSyavIZr+2E7soJ1Afgl5noMJdSqHMwRxg5ZoJk5tVqS+5U2e+lu/U6UvQpEaJroBO3VruM2Wx+rWaypF+fmaeevZWnv7n+4QFF1NaXEp+kYd//yOJhPZWXnn7O4qLy7j73kvh3ktPvEMOl0r4ZBmffbIAm1WjvFIy5qJBTJw0Br2+blPjw/+4mrffDOSLT5cw7uYchBAkJkUz/vbLGTmq5zGOUFePnomMGH0eV01YxND+FsorJBu2eRg9dhjvfb6VdjHBvP1hBQaTlTvv+SsXj+1XL462pNXUY2/N7eitWVlJEc/ceQf9/esuylzpKmefNZMnX3+H3Tu2sXTOLEoKCunYrSvnjB6Lpnl467lnyNuTjVbhwSEriQyIw2qx4YnwYLeXY9hvJFafiIYHs9VCWFQ0u+ybKbUUULS/gCAZgvRAOSX4iyCI0vj7v54nIblTs5L71x9OI+PHpcSI9uiFnlzPQdr1TGLiA//wSYW/T7I2c0nvOT7rPP2d2+3hgb+/S2TAPv5xX3ssFu97kV/g5NpJ2Xz61RPHvWo+2pwf1jHz06954clY2sWaKSp28cR/D9Gp+1BunTCywddomobL5UGv19Vbcq+xsrOLyVizCz8/M4MGp2GzWSgqrGDHjmzCwgNITY1t1SV8G1uP/bT/ypqZsVUldR+yWP2QQuL01NR53uGqIDAklHUrlvH2M89QvraMwENhZP6wgRceuQ8hBHc98Swek4fEiFTOSRxLamhP/EUg2bv24sytwa4rxywsWKQf1RXVHMraT6E7l+sm3YM50IxLOjHqzAQRSrksRp9v5JWHH+H7Tz88RrSNM+76W7n2vjux9fbH0NXAxXdcx/j7HmlTZVubwmDQU+2o4Kq/RBxJ6gCRESYiQnUcyj5+ZcWjffPVYu6bFE67WO8XQViokUfvjWbWt8uornaybOk25sxeV6dao06nw2w2NjmpA8TFhXLpX9IZeUFPbDZv82BYeACDBqeRlhbXqpP6yThtSwqoZpfTg9lipe+wYfy6eDOp/j0x6IxUue3s9/zKlWNuY+b70+ls6kOgyTtCIdQSwe7yLSya/S090wcQYgqnvTWFHcUbyS7bSwgRGF0m7KIcf2sg26oyiKQdUmrsd/yK0AtMZgsWPxtWzUZW2V5i9An0MQxDLw24qmv4+Ycf6T1wMPGJdZc1KyrIY9YnH7F1XQZmi4UBI0ZywV+urNe+LYSge590uvdJP2XvY2sRGxfJjl0HOKuL7chz5RVu8os8REYFN3o/RYXlJP1hoeiYKBP2SgfXXPEvOiUJwkJ0TH2jijFjhzHhtlFnTNI9FU67xH50Qledo6eHy24Yz+fON8lY+RNmYcWlczLq2itpl5SM2+Em0BZSZ/sIcxw7N25i8PmjqdbsFFTlkFd2gB66gRiEgVJ3MR6dk701O4gkjkKRgxQSt3BxdtB5fD71DXRSR5g1Cs2ukaz3dpZqeHC5IdQTybaN6+okdntFOS/94yH8igLoYumLcAkyvllC7sEDjL/vkVP6frVmnTon8swLK5g5K49RI8IYnB7Ea9MKOXdEv5OqNZPaOYHlqwq4dEztqJmMDRWUl1fxzMNRDB/mneFcXuHm1nuW071HMv0HdGrx8zlTnVaJvS0NX2xLTGYz10++h0uvv5mykmIioqIxW7yLcriks05HJIDDXUlQWCjRse1ol9KBzDXriCQegzDikW6EENh0wRjdRgIMwSToO5Hl2YO/LZAIv1j2FG3D5GemusyBRu3iE27pwmS1UCnKMfzhKnzOzM9w/FZJJHFUlJchhUZSeGe2rl/T4KpQRQV5bFizgmqHgy49epOUktYmrxg3bdzHooXr0TweBg3pTv8Bx+6fmPHRT8z/YQGP3x+Njmp+mJ/Pv1/K5ubxFx4uUdB4N94ymkfuf4OqKo1+vQPI3Ongf1OLiI2xHUnqAIEBBq4dF8Ci+Rkqsbeg0yKxt8ZZo2eigMAgAgKDjjy2+QfQY8AAdv2yhRT/7hh0BuyuCg5qu7nhQu+Cxbf8/SGenDweZ041dq0CdBAWGUl5aQlut5siLY8icrHry0kPHYFHepBCMvqqa/h2+vuUyWKiPSXeOwWc+AcEUSzz6XVU8S+Xy8XCb74mVkvCz+CdeeuRHkoLCrGF+ZN7qG5i37D6F2a89iohnkgM0sDyb+fQfWh/rpkwuU21s783fQEL5y7m8ov9MRlh+hsbWb60Jw88fHm9ouXHpgAAFpdJREFU5F5WauezGfP4fGo8EeEmJHDpRRoPPZ1Nu/jIOgtaNEZaWhwvvHwnn3+ymG/nHyQmLoKrrx/EqqUL6m1rtehwOl3NOVXlD3ye2FXHaOt29fhJfOKZQsaaxd7CXnonY66/Fk3T+PK9t7EFBjL2hpuYM/1Twi1RmE1WhBC4TS60Uo0ydyFJAV3oFTAYg87IrsotdOnTm6HnjyE0PIKv3p3Kpp0rCTNGYQsIJIs9XHnbbYRF1k7o2rFlPSYsVIvaqRR6oUfn0VPoyCMiqrauTHWVg0/emEJX49kE2LxfUolaKhuX/UJm/4F07dHn1L15f6KsrCK+n7mIL6YlEBzk/TMfO0rjujs2sHlTP3r0TKyz/fbtWXTtZCEi3Dv5RwB6vY4LzrUx75cdXDounVUrf2XhvDXUOJ0MGNiDkaN6Hjfhd0yJ4bEn/nrkcU2Niy8+nc/2nXbCQ40E+OsxmXR8M6eCkWMbHimjNI3PErtqdjk1CvJyWPvLMqrslXTu0YvUbj1b9KrUbLFy890PUFJUwM5tWwgIDOKn2d+Rk3mAEC0Sl6ihUJdLbEp7tuxdQ0h1OB6dm1JDIXf+82l2bdvCyvkLqK6x49Aqie2UwDUTJwPQrVc/ur3WD4e9kswtG9A0D2ndetW5awCorq4ixBpBQdUhcj0HiNTF4cbNPrkT//AA4tonHtl2945t+Gk2Aiy1+9DrDERosWxataLNJPaMNbsZNsB6JKkDWCzeMgGrV+2sl9iDQ2zk5LsOL2pdezWfk+siKCSQ6e/M5+eflvLXywKw+en4Zs53LP1pA/954dZGjwc3m40MOacv4274gfAQgdMlMZrM9OrXgxHnqwu7luSTxF5yuEaDukr/c3mbHF4jTIvEoBnJ+HEJHft246a77kevb/qQsj/as3M77738AtIuqXCU4i53kx49AswSk85MpDuOX7M3cfMjD7IncytWm41e6YMJCQunR9/+jLh4HNkH9xEcEkZsfEK9ZgI/mz99+g855vE7pnbDriunV/Rg9pRsY7/jV3Q6HcKqY9KkJ+psq9fr0ag/d0MTGnqDz29gW4zVaqK8ov55llZIAqLqj0VPS4vDZA3joy8KuO6KCHQ6wa69VXz6rZ17H+zM889+wNfvJRAU6H2Phg8NYfw9+/nl50yGDmvcTOA1q3ex5pdVfD49lbhoKCx2MmVaGTExoc0a4qjU55NPcpjRqpL6n6ymuopP3pxCV2NfAkzeYWoJshMbM1awZf3qk1qg4ngc9krefu5fdPB0IdwvmrVlS/F43CzPmo3JYMaNm2j/ePR+ekwmExde8dd6+wgODWtW8a2QsHBGXHYZP331HWHmKEIMERTr80np343uvesOaeyY1g232UlxdT6hFm9dcKenhnyy+Mvgm5scw+lm8JDOvDXlazZsrqBXd2+/w559Vcz7qZo3ptX923M63Xz4/iLycot49v8KeXFKFslJQThqjNw++SoqKqpJ72M9ktQBdDrBBedaWZuxs9GJfeaXPzH5lmB6dAsEIDwc/vVYMONuzmDC7WOOjDtXmq/tXKIodfy2eydWj40Av9qxxzqhJ1LEsXHFihZL7JvXrcLfGUi4v7cAqFOrplwrIVX0JECEIIXGb5WZlLqK6ixH19KGjhzDmmWL2bN7GwZpQli849U9Hk+dpiejycQt9z3ItOf/Q449AL00UEIBwy/7S4PL17VW/v4WHn/qZh5+6gOSE8owGgVbdzi5++9XExtbdynBF//7JdVl23nvf9GEh8Yz68cC3vzQzlvT7ia+fTi//LyDwiJPvWPkF3oIDGz8mrD5ecUkJ9UtXRwcZCDQX1BcXHkkse/Ykc3uX3OIiQ2hV++kNtWhfaqoxN5GGQwGPLJ+XXCPdGOyNG5aeGM47JUYPLVDDzWpEU17/PAHKTHojMSRTL4zm+DQ8OPs6cQ8Hg97dm7DYa8kqWMqQSG1V/lfv/8Ohjwj58degRACTXrYmpHB4h++4YJLr6yzn5TOZ/Hk69PYvmkt1dVVpHbtQXhkQ5WpW7c+fZP59KvHWZuxB4/Hw2N9k+tdFefklJCxahOzPk7CbPYm0KvGRZNbkMPcHzKYOGk0Z6d35NWXDMxbXMLIc4MRQrBjl4NZ86t49a3GlyLulJbIyjW7SelQOx5+774qqp0GoqODcTrdPPXPj9i3+1f69LAwe6YLj/BWdgwNC2iZN+UMoRJ7G5XUMQ3hL8i3ZxPp5+2cdnqqyRNZjB16fYsdp2NaN37Uf4FHc6PXGUCCvz4Qh2bHhBk0wADhQdE47BX1Oj4bKy8ni7f+8wzO4hpMwkK5VsyIceMYNe4q3G43G1au4Gz/8460z+uEnkRLKisXLqyX2MG7mHefAUObc+otqtj+5xRHNZuNDBqcdszfH9hfQGqy5UhS/13Pbla+XZQNgNFo4Nn/TuCpf77Pu5/sx2bTkZUDd993bZ2yvSdyzV/P4947tyJ0eQwdEMRv+6t5bXoJ1990MUajgfffXYTBvZev3k3CYBBIKXnj3Tz+99JMnnr2xqa9AWcoldjbKL3BwIQHH+Wt/zxNrv0gRkyUykJGjBtHSufaOtT2ygp+XjiX7evWExAczJBRo0nt2uM4e64rPjGZnsMGsnHJL0SL9uj13qaNxKA0/Gz+6PQ63DoXBr2R0PCm1ZyXUjLtxecIKg6jna0D4P2SWvL1LBJTOtGhU2c0j4Ze1P04G3RGnDWn/2IK17brzkG+8smx2ydEsHNPNTU1Wp3kvnFrFfHta9vO9+7JQ/N42H3AjtXPyo23XMCwc0+u7G1CYiQvvXYXMz5cwMy5vxEeEcz4O8YcaaNfvGA1zz4cjsHg/XIWQnDzNRGMvno7VVXOBuuwKw1Tib0NS+iQwtOvTydzywaqqxykdO5GSFjEkd877JX83z8eRORBpDGOyr3lTF/3HBfddD1DR554puGWDWtY+sNsyoqKie2WiB4DHbRUdm3fSpksxmyyUO6q4KB7N5dOuKnJNcmzD+yjIq+UTn61XzgmvYUY2rNy8UI6d+9NUmoqh3bvo51/hyPbZDn2ctZ5qh7M8cTEhND37LN47Nkd3HN7FOGhRuYuKmbOYhdvvOPth1m6ZBsfTPucpx+K5KwukezdX81TLyzAYNBz5dXHHq3UkMSkyDpj24/mdLqxWnS43ZIZX+Qye0EhDoeH/AKN3NxSkpIiG3ydUp9K7G2c0WQ6ZrGrXxbPg3zoHFg7djvUFcmsGR+SPvRczJZj1wZZOm82cz74lHa6ZKIN7cnPzcIeVMED/3kRt9vNwu9nsnvrVoLDwrlp7P10acaycE5nNQZhrDcM0qAzUePwLpN35fjbeO3Jx7FXluMn/SkXJchwyZjLr27ycc8UDzxyFe9PX8Ct966kvLyKPv068fxLFxEd7e14/3zGAh6YHEb3rt6O0uREK088EMXfHl3E5VcOarHOzQGDu/Pl95uorKimtNTBMw+GIoTky1l2Hn1gKm+/e99J1as5k6nEfgbbsX4DEfq6Ffj8jP6YXX5kH9xHh5TODb6upqaa2Z/M4CxLOn5G7x97kDmUX8s3s2TeD1xy9Q1cdeukFoszPrEjLqOTcmfJkSqS3sW6s7hggLf9PDY+kUdfnsLqZYvIzz5Ev47n0Hfg0GMuIK3UMpkMTJw0momTRteboASQnV1Al9S6kwg7JFiorLBTXe1qdI32E7nhxhFMvHULhblFzJoRi9QEdofggbs68J9X8pkzey1XX3tydwhnKpXYz2BBYaEU7syr85wmNWo0B/4Bx+7kzM/JxqiZ8LPUHeoWboxm9+Yt0MIXyUajkatvv4MZr75GeHU0ZmGhiDwiUmM4e9C5R7YLCAxq9pqlZ7qGCoR1SI5l7cYKzj+ndpjk1kwHoWFBLdruHRLqz8RJF7Pg+89B2DBZDERE+aHX60jvY2X5xgMtdqy2TiX2M9jgC0bz5sqnCHVG4m8KQpMaeyu3E5+aTGR07DFfFxgUTLXHgUfzoNfVzhi0u8sJiYw45uuao9fZg4h5PoHVyxZTWVrG0J4X0qNvfwyG03ct0bbiuhtH8e+npgHQr1cgmb/aeX5KEdfffEWLV8RMSIgkO1cQERGETle778xdNcTEqjb2xlKJ/QzWIaUzl98+kZnvTUfvMOD0VJPYpRM33nX/cV8XFBJG1759+DVjEyn+Z2HQGSmvKeYQ+xmaPoYfv/mcKrudtO49W7Q2TXRsOy65+oYW2ZfSeH36JvPoE+P56P0f+e+ULGJjwxl/x1855yRHxTRGSqcYomLjeWHKISbdHIXNT8/8n0qY95OLt949u8WP11b5ZM3Tjqld5fNTPz3lx1Ua5nI6yck+gM0WUKdq4vFUVzn47J032Lx6NUZhwuBnoMeAAaxZvIRQdyRGaaRAHCK8QwyX3TCepJQ0NYPwOA7a/smNaZf7OozTQkVFFa+/+h3LlmwEqZGcEs/ku8eRlqaKBTZ2zVOV2JVmsVeUY7dX4h8YyJN3TCBN9CbQFMKekm3sLdmOQTNhDrEQ1j6S2x5+vE3O8GwJKrHX53S6cbncqobMURqb2FVTjNIstoBAbAGB7Ni6EYvHj0C/EAochzhQspvuuoEgQOfWUZVXyfT/e44Hn3v5uO2yzpoali+cy/rly9HpdfQfPpz+Q0e0qcqLSuOYTAZMJvXv3hTq3lhpEd7aNN5CUVnle4khAZMwI5HodHribR0pysonJ/vYIxs8Hg9vPvcUyz7+gaCcMGwHA5n7zud8+PrLp+o0FKVNUF+HSos4ujaNW3NhFCYkGm6cBAeEIYTApDNRU1V1zH3s2LKBvF+z6RUw+MhVfYglknVrlnJw3x7iE5P5vemwLa5PqigtRSV2pUUcXZum2uAg27EXozQRFBKKxWKlwlmKy+iiXWLyMffx266dBHnC6iRtvdATpIWyed0qfvzqc7atX4vBYCT93PO46KrrsfqpCUiK8kcqsSst5vfaNJvXr+abD6ZTVJyL3qCnqCKXfN0hrr3zzuPWiwkKCcGpq72ir65yUFJUyP6q3ex4cwMd/c4iPdK74HXm/I3kHDzA3x7/l7p6V5Q/UIldaVFGk4k+/YfQvXc661f/TOaGDQSGBHPdOXcRG594zNd53G5MZguHqvZhcliI9m9PUV4+5bIYt85JNPEEV4VTWVpGSFgEnQN6s3bnEvbv3UVicqc6+zq4bw+7Mrdg9fOnR9/++NkavxiEorQFKrErfwqjyUT6kPNIH3LeCbd1OZ28+dzT5O3IIkafwO7yrWwvW4de6Am2hhNtiMfqsGHV+1FZXkFQSBg6nY4AEUxB7qEjiV3TND6b9gYbl/5CsCcct97NN++/y8SHHqNjWuOWb1OUtkCNilF8btXShRRm5tDTfxBdwvoyMvFKkkxp6PQG+sedT6g1kgpKEegQCDweN1JKymUJUTG1k1a2rF/DliWr6eM3jE5BPeji34dkTxfee/kFPO76q0kpSlulErvicxtWrCDa0P5IW7kQghhbIgbNQIWzlLiAJOz6cg64d+PGiZMaMivW0b5zR+KTOh7Zz7rly7yLfehqb0TDrNHggP17d53y81IUX1GJXfE5k9lcb33WgKAgnKKGXMdBNKmRGtaLXNMBdvltYbvMoNuYfky4/5E6HaeSUz+LWlFOR6qNXfG5/sOH89nmN4nQYjHovKNmClzZxKclE9Yuks07VxAYGcotEx6k/7DhxxwF02fwEL7IeItorf2RqpNF1XlIqyShQ8opOx9F8TWV2BWf69F3AHtHZbJi3nyCRBhOqtEF6Zj06JNEx7Zr9H669+nPtqFrWbd8KcHucDwGFxXGUibc+6gqSaCcUdSnXfE5IQTjbriVIReM4bddO7AFBJLWtcdJJ2OdTse1t/2NwSNHs2v7Fqw2Gz37DsAWEPgnRa4opyeV2JXTRkRUDBFRMSfe8DiEECR0SFFNL8oZTXWeKoqitDEqsSuKorQxLZLYhRD3CyGkECK8JfanKGeaTWUdmXVwjq/DUNqIZid2IUQ8cD6glhBXlCYqz+3p6xCUNqQlrthfBh4ENTtEURTldNCsxC6EGAtkSyk3NWLbiUKItUKItWVlJc05rKIoinIcJxzuKIRYCDS0AvFjwKPAyMYcSEo5FZgK3sWsTyJGRVEU5SScMLFLKUc09LwQ4iwgCdh0eIp3O2C9EOJsKWVui0apKIqiNFqTJyhJKbcAkb8/FkLsA/pKKQtbIC5FURSlidQ4dkVRlDamxUoKSCkTW2pfiqIoStOpK3ZFUZQ2RiV2RVGUNkYldkVRlDZGJXZFUZQ2RiV2RVGUNkYldkVRlDZGJXZFUZQ2Rkh56su2CCEKgP0n8ZJwoC3MaFXncfppK+eizuP082ecS4KUMuJEG/kksZ8sIcRaKWVfX8fRXOo8Tj9t5VzUeZx+fHkuqilGURSljVGJXVEUpY1pLYl9qq8DaCHqPE4/beVc1Hmcfnx2Lq2ijV1RFEVpvNZyxa4oiqI0UqtI7EKIF4QQO4QQm4UQ3wghgn0d08kQQowSQuwUQuwWQjzs63iaSggRL4T4SQiRKYTYJoS429cxNYcQQi+E2CCEmO3rWJpKCBEshPjq8N9HphBigK9jaiohxL2HP1dbhRCfCiEsvo6pMYQQ7woh8oUQW496LlQIsUAIsevw/0NOZUytIrEDC4BuUsruwK/AIz6Op9GEEHrgdWA00AW4RgjRxbdRNZkbuE9K2RnoD0xuxecCcDeQ6esgmul/wI9SyjSgB630fIQQccBdeFdh6wbogat9G1WjvQ+M+sNzDwOLpJQpwKLDj0+ZVpHYpZTzpZTuww9X4V1ftbU4G9gtpdwrpXQCnwGX+DimJpFS5kgp1x/+uQJvEonzbVRNI4RoB1wITPN1LE0lhAgEhgLTAaSUTillqW+jahYDYBVCGAA/4JCP42kUKeUyoPgPT18CfHD45w+AS09lTK0isf/BLcBcXwdxEuKAg0c9zqKVJsOjCSESgV7Aat9G0mSvAA8Cmq8DaYYOQAHw3uEmpWlCCJuvg2oKKWU28CJwAMgByqSU830bVbNESSlzwHtBxFHrQ58Kp01iF0IsPNy29sf/Ljlqm8fwNgfM8F2kJ0008FyrHookhPAHvgbukVKW+zqekyWEuAjIl1Ku83UszWQAegNvSil7AXZO8S1/SzncBn0JkATEAjYhxHW+jar1arE1T5tLSjnieL8XQtwIXAQMl61rjGYWEH/U43a0klvMhgghjHiT+gwp5Uxfx9NEg4CxQogxgAUIFEJ8LKVsbYkkC8iSUv5+1/QVrTSxAyOA36SUBQBCiJnAQOBjn0bVdHlCiBgpZY4QIgbIP5UHP22u2I9HCDEKeAgYK6V0+Dqek5QBpAghkoQQJrwdQt/7OKYmEUIIvO25mVLKl3wdT1NJKR+RUrY7vAD71cDiVpjUkVLmAgeFEKmHnxoObPdhSM1xAOgvhPA7/DkbTivtCD7se+DGwz/fCHx3Kg9+2lyxn8AUwAws8P6bs0pKebtvQ2ocKaVbCHEnMA9vT/+7UsptPg6rqQYB1wNbhBAbDz/3qJRyjg9jOtP9DZhx+KJhL3Czj+NpEinlaiHEV8B6vM2tG2gls1CFEJ8C5wDhQogs4AngOeALIcSteL+0rjilMbWuVg1FURTlRFpFU4yiKIrSeCqxK4qitDEqsSuKorQxKrEriqK0MSqxK4qitDEqsSuKorQxKrEriqK0MSqxK4qitDH/D0y8Dq5V5ifNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d478e5ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os \n",
    "os.chdir('../')\n",
    "from ml_models import utils\n",
    "%matplotlib inline\n",
    "utils.plot_decision_function(X, y, lvq)\n",
    "utils.plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意，这个算法的监督信息只给了两类，但最后通过聚类得到了4类，也许是我个人认识浅薄：这直接对每个类做个2类的kmeans聚类不就可以了吗~~~，似乎该算法的作用不是很大..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
